JP2019511070A - System and method for analyzing nucleic acids - Google Patents

Abstract

  Provided herein are systems, software media, networks, kits, and methods for performing computer analysis on sequencing data of a sample from an individual. The analysis can extract germline and somatic information, compare both types of information, and identify sequence variants based on probabilistic modeling and statistical inference. The analysis may include distinguishing between germline variants, eg, variants of individuals, and somatic mutations. The identified variants can be used in the clinic to provide better health care.

Description

Cross reference
[0001] This application claims the benefit of US Patent Application No. 62 / 293,136, filed Feb. 9, 2016, which is incorporated herein by reference in its entirety.

  [0002] Accurately identifying cancer somatic cell mutations from high throughput sequencing data of tissue samples can be a difficult open task. Sequencing data can be used in clinical treatments for treatment selection for which the analysis rate of false positive or false negative variants is unknown. Tissues that may be encountered in this process include tissue sample heterogeneity due to the presence of a wide range of normal cells that differ from sample to sample (eg, primary tumor vs. cell-free DNA (cf-DNA) in plasma), The presence of multiple clones of cancer cells in different proportions, lack of data obtained from samples of 'normal' tissues, which allow differentiation of somatic and germline variants, pathological treatment ( For example, damage to DNA in samples by formalin fixed paraffin embedding (FFPE)), as well as rotation of structural polymorphisms and simple sequence variants. New analysis methods can improve the identification of germline variants from large-scale sequencing data.

  [0003] In some cases, cancer data analysis may produce inconsistent results when data in the analysis is compared to a single control sample. In some cases, data analysis relies on the validity of data obtained from normal tissues of patients treated similarly to the sample containing or suspected of containing cancer cells, which It is often unavailable in the use case of malignant tumor pathology. Current analysis pipelines, including manual or heuristic methods to exclude germline variants from somatic mutations, are arbitrary, inaccurate, difficult to reproduce, and implicit in the process. May not provide information on the false positive and false negative trade-offs that occur. However, when normal tissues are available, in some cases they are analyzed independently and are combined for the first time as a filtering step after decisions on "real" germline variants are made, False positive somatic mutations due to germline variants outside the threshold imposed on lineage calls may be called. A solution that addresses the latter problem may be to use a panel of normal samples as a reference germline variant common to the population. New methods are disclosed herein to further address rare variants present in the patient's body, including cancer sensitive variants. The method can be based on simultaneous calling and scoring of variants, from sequencing data that aligns all samples obtained from the patient, as well as other series of previously analyzed patients.

  One embodiment of the present invention relates, for example, to a system and method for analyzing nucleic acids.

[0004] Provided herein are systems, software media, networks, and methods for identifying cancer somatic mutations from tissue high throughput sequencing data.
[0005] In one aspect, a computing system is disclosed herein, the computing system comprising: (a) a processor and a memory module configured to execute machine readable instructions; (b) A data analysis application comprising: (1) a data receiving module configured to receive a sequence read of nucleic acid molecules obtained from one or more samples of an individual generated by a high throughput sequencing instrument And (2) sequence alignment module configured to align the sequence reads to a reference assembly to generate a predicted genomic sequence, and (3) (i) by jointly analyzing the predicted genomic sequence Identify putative variants and (ii) somatic mutations or The estimated variant by probability of germline mutants and a genomic analysis module configured to scoring.

  In another aspect, disclosed herein is a computer-readable storage medium encoded with a computer program comprising instructions executable by a processor to create a data analysis application, the application being (A) a data receiving module configured to receive a sequence read of nucleic acid molecules obtained from one or more samples of an individual generated by a high throughput sequencing instrument; (b) a reference assembly of the sequence read And (c) (i) identify putative variants by jointly analyzing the predicted genomic sequences simultaneously, and (ii) Probability of being a somatic mutation or germline mutation Therefore and a genomic analysis module configured to score the estimated mutants.

  [0007] In another aspect, a method is disclosed, the method comprising: (a) collecting one or more samples of an individual; (b) using one or more high-throughput sequencing instruments Sequencing the sample nucleic acid molecules to generate a sequence read, (c) aligning the sequence read to a reference assembly to generate a predicted genomic sequence, and (d) jointly combining the predicted genomic sequence. And (e) scoring the putative variants by their probability of being a somatic mutation or germline variant by analyzing simultaneously in.

  [0008] In various embodiments, the systems, software media, methods, or uses thereof disclosed herein comprise using one or more samples. One or more samples can be collected simultaneously. In some cases, one or more samples include at least two samples, and at least two samples can be collected at different times. In certain applications, one or more samples may include one or more of primary tumor, metastatic tumor, fluid, cell free sample, lymphocytes, and plasma.

  [0009] In the various disclosed systems, software media, and methods disclosed herein, identification of putative variants can be performed by sequencing a bank of sequences obtained from one or more previously analyzed patients with genomic sequences. Can include comparing with Scoring of putative variants can include adjusting the probabilities based on machine learned methods trained using the correct and false call pairs. Identification and scoring of putative variants can involve making inferences at chromosomal loci.

  [0010] In various applications, creating inference can include using one or more of a probabilistic model, statistical inference, Bayesian estimation, and Bayesian network model. In some designs, generation of inferences includes prior probabilities of germline and somatic mutation detection, a series of sequence reads aligned across chromosomal loci, error rates of high-throughput sequencing instruments, chromosomal regions including chromosomal loci Call of polyploidy, process model of clonal evolution of cancer, calls at chromosomal loci derived from one or more other samples of individuals, calls at chromosomal loci derived from one or more samples of one or more other individuals Prior knowledge of common polymorphisms at chromosomal loci of one or more reference populations, prior knowledge of mutations of one or more recurrent cancers at chromosomal loci, proportion of cancer cells in a sample containing cancer, Stochastic model description of variants, Stochastic model description of a series of aligned sequence reads across chromosomal loci, Stochastic model chromosomal loci Description of definitive ploidy, and can be based on one or more of the described ratio of cancer cells in a sample by a probability model.

  [0011] In some designs, an error rate may be introduced in quality verification for base calls. The sample containing cancer may contain one or more DNA molecules that cause cancer, or one or more cancerous tissues, or both. The ratios used herein can be described by binary variables.

  [0012] In the various disclosed systems, software media, and methods disclosed herein, the data analysis application includes one or more coding regions, predicted damage severity, one or more germline lineages Mutation, one or more somatic mutations, one or more mutations or interactions between drugs, one or more mutations observed in a clinical trial, one or more diseases, one or more symptoms, or The method may further comprise a module configured to annotate the putative variant for an effect on one or more of the one or more side effects.

  [0013] In the various disclosed systems, software media, and methods disclosed herein, the data analysis application can comprise a module configured to recommend a treatment method, a treatment method, or both. .

  [0014] In the various disclosed systems, software media, and methods disclosed herein, a data analysis application can comprise a module configured to assess the progress of a treatment.

  [0015] In the various disclosed systems, software media, and methods disclosed herein, a data analysis application may comprise a module configured to assess risk.

[0016] In the various disclosed systems, software media, and methods disclosed herein, the data analysis application comprises a module configured to monitor the efficiency of the treatment method, or treatment method, or both. Can.
Incorporated by reference
[0017] All publications, patents, and patent applications mentioned in the specification are individually identified as if each individual publication, patent or patent application was specifically incorporated by reference. To the same extent, they are incorporated herein by reference.

  The novel features of the invention are set forth with particularity in the appended claims. The features and advantages of the present invention will be better understood by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, as well as the accompanying drawings.

[0019] FIG. 7 illustrates the method disclosed herein. [0020] FIG. 6 illustrates an example of a data receiving module. [0021] FIG. 6 illustrates an example of a sequence alignment module. [0022] FIG. 1 shows an example of a genome analysis module. [0023] FIG. 7 shows an example of sequence analysis at a chromosomal locus. [0024] FIG. 7 shows an example of using different types of samples from a subject to assess the probability of a putative variant. [0025] FIG. 7 shows an example of using information around loci to assess the probability of putative variants. [0026] FIG. 7 is a diagram of a Bayesian network for simultaneous inference of cancer somatic mutations. [0027] FIG. 10 illustrates a computer control system that implements the analysis disclosed herein. [0028] FIG. 1 illustrates an exemplary workflow of a method of creating a DNA library, eg, from a tumor sample of a subject.

I. Overview
[0029] The techniques disclosed herein may be directed to computer analysis of high throughput nucleic acid sequencing data of a sample from an individual. The analysis can extract germline and somatic information, compare both types of information, and identify sequence variants based on probabilistic modeling and statistical inference. Germline variants refer to nucleic acids that contain natural or normal mutations (eg, skin color, hair color, and normal weight). Somatic mutation refers to nucleic acid containing acquired or abnormal mutations (eg, cancer, obesity, symptoms, diseases, disorders, etc.). The analysis may include distinguishing between germline variants, eg, variants of individuals, and somatic mutations. The identified variants can be used in the clinic to provide better health care.

  [0030] As used herein, improved methods capable of distinguishing sequence errors in nucleic acids introduced by amplification and / or sequencing techniques, somatic mutations and germline variants, computing Provide a system or software medium. Methods are provided that include simultaneous calling and scoring of variants from sequencing data in which all samples obtained from the patient are aligned. Samples from other subjects, such as samples from other subjects that have been previously analyzed by sequencing assays, such as target sequencing assays, such as target resequencing assays, can be used. Better discrimination of germline and somatic mutations (eg, fewer false positives), and lower detection limits (eg, fewer false negatives) by use of improved methods, computing systems, or software media Can bring

  [0031] FIG. 1 shows an overview of the method provided herein. At step 101, the system or method includes collecting one or more samples of the individual. The sample can be obtained, for example, from an individual, such as a subject, a patient, from tissue or body fluid or both. The sample can be any sample described herein, such as a primary tumor, a metastatic tumor, a buffy coat (eg, lymphocytes) from blood, or cell free DNA (cf-DNA) extracted from plasma. . At 102, sequencing of nucleic acid molecules of one or more samples can be performed, for example, by a high throughput sequencing instrument. For example, one or more sequencing libraries can be prepared by any of the methods described herein. Sequencing libraries can be prepared for each tissue sample and / or for samples obtained at different times. Sequence reads can be generated by sequencing. In order to assemble the sequence reads into the predicted genome of the individual, step 103 aligns the sequence reads into a reference assembly, for example a human reference assembly, to generate a predicted genomic sequence. At step 104, the system or method identifies putative variants. The identification can include jointly analyzing the predicted genomic sequence simultaneously and scoring the putative variants by their probability of being somatic mutations or germline variants. As described herein, estimates of cellularity of a sample can be used to provide scoring information. Variants can be re-scored based on, for example, machine learning methods trained using a series of good (ie true positive) and bad (ie false positive) cells. Variants include coding regions, severity of expected damage, cross-referencing of other databases of germline and somatic mutations, interactions between mutations and drugs, clinics accepting patients with mutations observed Annotations can be made regarding the effects of the variants in a test or other medically relevant knowledge base. At step 105, the tumor board makes a treatment recommendation for the individual, providing the tumor board with variant information and annotations, eg, evidence that no mutations exist across the oncogene and associated hotspots, or It may be possible to assess the course of treatment or possible relapse.

  [0032] Also, as used herein, nucleic acid molecules obtained from one or more samples of an individual generated by a processor, a memory module configured to execute machine-readable instructions, and high throughput sequencing equipment. A data analysis application comprising a data receiving module configured to receive a sequence read of a sequence, and a sequence alignment module configured to align the sequence read with respect to a reference assembly to generate a genomic sequence (i A genomic analysis module configured to identify putative variants by jointly analyzing the genomic sequence jointly and (ii) to score the putative variants by probability of being a somatic mutation or germline variant Computing system To provide.

  [0033] Also herein, a computer readable storage medium encoded with a computer program comprising instructions executable by a processor to create a data analysis application, the application comprising a high throughput sequence. The sequencing leads are aligned to a reference assembly to generate genomic sequences, with a data receiving module configured to receive sequencing reads of nucleic acid molecules obtained from one or more samples of the individual, generated by the Identifying putative variants by (i) jointly analyzing the genomic sequence jointly with a sequence alignment module configured to generate, and (ii) estimating by probability of being a somatic mutation or germline variant Scoring variants And a genomic analysis module configured urchin, a computer-readable storage medium.

[0034] Also, as used herein, the steps of collecting one or more samples of an individual and sequencing the nucleic acid molecules of one or more samples using a high-throughput sequencing instrument to sequence read The steps of generating, aligning the sequence reads to a reference assembly to generate a genomic sequence, and co-simultaneously analyzing the genomic sequence to identify putative variants, somatic mutation or And Scoring a putative variant by its probability of being a germline variant.
II. Data analysis application
[0035] The methods, computer systems, or computer readable media provided herein can include one or more data analysis applications. Data analysis applications can comprise several modules with different functions. For example, the data analysis application may comprise a data receiving module that receives the sequence read. The data analysis application can comprise a sequence alignment module that can obtain sequence reads and align the sequence reads to generate a predicted genomic sequence. The data analysis application can comprise a genomic analysis module that can obtain predicted genomic sequences and perform probabilistic and statistical analysis to identify putative genetic variants that cause disease.

[0036] A. Data receiving module
[0037] FIG. 2 shows an example of a data receiving module. The data receiving module 201 can include a temporary data storage device 202 such as a memory device or hard drive that stores sequencing reads, for example, sequence reads generated by high throughput sequencing device 211. Non-sequence data 212 may be provided to data receiving module 201. Examples of non-sequence data 212 include, but are not limited to, name, date of birth, gender, age group, medical history, family information, sample source, sample collection time, and biological state of the sample. The data receiving module may receive sequence read data from at least 1, 2, 3, 4, 5, 10, 20 or more samples from the subject. The data receiving module may receive sequence data from at least one, two, three, four, five, ten, twenty or more different subjects.

  The data receiving module may include a data reorganization process 203. The reorganization process 203 can reorganize the temporarily stored data into a predetermined format and store the reorganized data in the database 204. For example, sequence leads of multiple subjects can be separated for each individual subject. In another example, sequence reads can be reorganized based on the annotated information. In some embodiments, for example, if sequence data and non-sequence data can not be paired, the data reorganization process 203 may return both data to a temporary data store and wait for more incoming data. The data reorganization process 203 can mark missing data inputs and store the reorganized data in the database 204.

[0039] B. Sequence alignment module
[0040] FIG. 3 shows an example of a sequence alignment module. The operation of the sequence alignment module can include three steps. The module can access the sequence lead 311 from the data receiving module. Modules can also access one or more reference genomes 312 for alignment. The first step 302 may search for sequence reads and compare the sequence reads to a plurality of candidate chromosomal segments. "Plurality" can include at least two elements. In particular cases, the plurality is at least 10, at least 100, at least 100, at least 10,000, at least 100,000, at least 1,000,000, at least 10,000,000, at least 100,000,000, or at least It can have 1,000,000,000 or more elements. The comparison can be based on statistical analysis. At the second 303, the sequence alignment module can pick the genome segment with the highest match score. Steps 302 and 303 can be repeated for each sequence read. The final step 304 may, for example, assemble and aggregate all the sequence reads into an individual's predicted genome sequence, once all the sequence reads have been mapped to the reference genome.

  [0041] Genomic sequence, as used herein, can refer to a sequence that occurs in the genome. As RNA is transcribed from the genome, this term is intended to encompass the sequences present in the nuclear genome of an organism, as well as the sequences present in the cDNA copy of RNA (eg mRNA) transcribed from such genome. it can.

[0042] A predicted genomic sequence, as used herein, can refer to a genomic sequence assembled by a sequence alignment module.
[0043] In the process of sample preparation and sequencing, partial or complete sequencing of nucleic acids, eg, DNA fragments present in a sample, can be performed. Sequence tags can be counted that include leads that map to a known reference genome. In some cases, only sequence reads that uniquely align with the reference genome can be counted as sequence tags. In some embodiments, the reference genome is genomic. ucsc. edu / cgi-bin / hgGateway? Human reference genome NCBI36 / hg18 sequence available at org = Human & db = hgl8 & hgsid = 166260105. Other published sequence sources include GenBank, dbEST, dbSTS, EMBL (European Institute of Molecular Biology), and DDBJ (Japan DNA Databank). The reference genome can also include an artificial target sequence genome comprising human reference genome NCBI36 / hg18 sequences, and polymorphic target sequences. In some embodiments, the reference genome is an artificial target sequence genome comprising a polymorphic target sequence. The reference genome can be a public human genome (eg, hg18, hg19, or hg37).

  [0044] In some cases, the reference genome is the same disease (eg, cancer), age, ethnicity, gender, nationality, occupation, exposure (eg, to toxins, radiation, or biological agents) of the subject under which the sample is being evaluated. Or from subjects or groups of subjects of residence (eg, the same house, city, state, country, or continent). In some cases, the reference genome is a different disease (eg, cancer), age, ethnicity, gender, nationality, occupation, exposure (eg, to a toxin, radiation, or biological agent) different from the subject whose sample is being evaluated, or It is from a subject or group of subjects at a residence (eg, the same house, city, state, country, or continent). The reference genome can be from one or more relatives (eg, a father, a mother, a brother, a cousin, or a grandparent) of the subject whose sample is being evaluated. In some cases, the reference genome is not from the relatives (eg, father, mother, sibling, cousin, or grandparents) of the subject whose sample is being evaluated.

  [0045] The mapping of sequence tags can be accomplished by comparing the sequence of tags to that of a reference genome to determine the chromosomal origin of the sequenced nucleic acid (eg, cell free DNA) molecule. Without limitation, BLAST (Altschul et al., 1990), BLITZ (MPsrch) (Sturrock & Collins, 1993), FASTA (Person & Lipman, 1988), BOWTIE (Langmead et al., Genome Biology 10: R25.1 ~ A number of computer algorithms are available for sequence alignment, such as R 25.10 [2009]) or ELAND (Illumina, Inc., San Diego, Calif., USA). In one embodiment, the nucleic acid molecule can be clonally propagated, and one end of the clonally propagated copy of the DNA molecule can be used for efficient large scale alignment (ELAND) software of a nucleotide database, for Illumina Genome Analyzer , Sequencing and processing by bioinformatic alignment analysis. Additional software may include SAMtools (SAMtools, Bioinformatics, 2009, 25 (16): 2078-9), and block sorting or pre-processing to make compression more efficient, Burroughs-Wheeler block sort compression The procedure is mentioned. The sequence alignment tool includes Artemis Comparison Tool (ACT), AVID, BWA-MEM, BLAT, DECIPHER, GMAP, Splign, Mauve, MGA, Mulan, Multiz, PLAST-ncRNA, Sequerome, Sequilab, Shuffle-LAGEN, SIBsim4, or SLAMAM. Can be. The sequence alignment tool can be a short read sequence alignment tool, for example, BarraCUDA, BBMap, BFAST, BigBWA, BLASTN, BLAT, or Bowtie.

[0046] C. Genome analysis module
[0047] FIG. 4 shows an example of a genome alignment module. The input of the genomic analysis module can be genomic sequence 411 from one or more germline samples, genomic sequence 412 from one or more somatic cell samples, and prior genomic knowledge 413. Germline samples can include body fluids such as peripheral blood. The somatic cell sample can include tumor tissue. The prior genomic knowledge 413 may be information from a database of published scientific literature, or information from a database of genomic annotations, or information from a database of previously analyzed samples from the same or different subjects, or Information from combinations of databases can be mentioned.

  [0048] The genome analysis module can identify one or more putative variants by comparing the genomic sequence to a sequence in a bank of sequences from one or more previously analyzed patients. The module can perform four steps. The first step 402 may involve extracting genomic sequences from the genetic region, the sequences being from different samples. Step 403 can compare the extracted sequences across germline and somatic samples, and the comparison can be based on stochastic and statistical methods. Step 404 can determine one or more putative variants, which can be germline variants or somatic mutations. Steps 402, 403, and 404 can be repeated across all genetic regions of interest. Step 405 can assess the clinical meaning of one or more putative variants.

  [0049] Genetic regions can include one or more chromosomal loci. The genetic region can be a continuous region on a chromosome. The genetic region can be a collection of two or more discrete chromosomal regions. The genetic region can be on a single chromosome. In some cases, the genetic region can be on more than one chromosome. In some embodiments, the genetic region can be one or more base pairs.

  [0050] Comparison of sequences across germline and somatic cell samples, and determination of one or more putative variants, scoring of the putative variants by somatic mutation or probability of being a germline variant Can be based on Scoring of putative variants can include adjusting the probability based on machine learning methods trained with a set of correct (i.e., true positive) and false (i.e., false positive) calls.

[0051] D. Create inferences at chromosomal loci or genetic regions
[0052] Identification and scoring of putative variants can include making inferences at chromosomal loci or genetic regions. The creation of inference can include using a probabilistic model and / or statistical inference. Examples of probabilistic models and statistical inferences include, but are not limited to, Bayesian inference and Bayesian network models. The creation of inference can be based on prior probabilities of finding germline and somatic variants derived from prior genomic knowledge 413.

  [0053] The term "locus" can refer to the position of a gene, nucleotide, or sequence on a chromosome. An "allele" of a locus can refer to alternative forms of nucleotides or sequences at the locus. A "wild-type allele" can refer to the most frequently occurring allele in a population of subjects. In some cases, "wild-type" alleles are not associated with disease. A "mutant allele" can refer to an allele that has a lower frequency of occurrence than a "wild-type allele" and can be associated with a disease. In some cases, a "mutant allele" is not associated with a disease. The term "interrogated allele" can refer to an allele for which the assay is designed for detection. The terms "single nucleotide polymorphism" or "SNP" can refer to a mutation of one type of genomic sequence caused by a single nucleotide substitution within the sequence. "SNP allele" or "allele of SNP" can refer to alternative forms of the SNP at a particular genetic locus. The term "discriminating SNP allele" can refer to a SNP allele for which the assay is designed for detection.

  [0054] Inference creation can be based on a series of multiple sequences across chromosomal loci. Referring to FIG. 5, chromosomal locus 501 is of interest. Multiple sequences can be from a single sample, and can be collected from multiple regions A, B, C, D, including locus 501. The multiple sequences can be from multiple samples 1, 2, ..., N, and can be collected from the same region C that contains locus 501.

  [0055] The creation of inference can be based on the error rate of high throughput sequencing equipment. Error rates may be introduced in quality verification for base calls. In some instances, the generation of inference can be based on the multiple relationships of chromosomal regions across chromosomal loci. Abnormal polyploidy may be associated with somatic or germline mutations.

  [0056] Inference creation can be based on a process model of clonal evolution of cancer. The process may be modeled by a Markov chain in which a second state is predicted or inferred from a first state. For example, the time of evolution from one stage of cancer to another, the size of the tumor tissue as the tumor evolves with time, the transition process from the primary organ to another distant organ, concomitant symptoms occurring in the early and late stages Cancer growth process.

  [0057] Inference creation can be based on calls at chromosomal loci derived from one or more other samples of the individual. Referring to FIG. 5, samples 1, 2, ..., N can be collected from a single tumor tissue of an individual, and the nucleic acid call of locus 501 is all available samples or available It can be based on the evaluation of germline mutations or somatic mutations by analyzing some of the possible samples.

  [0058] Inference creation can be based on calls at chromosomal loci derived from one or more samples of one or more other individuals. Referring to FIG. 5, samples 1, 2, ..., N can be collected from two or more individuals, and a nucleic acid call at locus 501 can be either all available samples or a portion of available samples. The analysis can be based on the assessment of germline mutations or somatic mutations call.

  [0059] Inference creation can be based on prior knowledge of common polymorphisms at chromosomal loci of one or more reference populations. Referring to FIG. 5, chromosomal locus 501 can be a known cancer that results in polymorphism in prior genomic knowledge, eg, prior knowledge indicates one or more recurrent cancer mutations in chromosomal locus 501.

  [0060] The generation of inference can be based on an estimate of cellularity to the proportion of cancer cells in the sample. Cellularity can be a fraction of nucleic acids in a sample derived from a tumor.

  [0061] Inference creation can be based on one or more probabilistic models. A probabilistic model can be used to describe a series of aligned sequence reads across chromosomal loci, a multiple relationship at chromosomal loci, or the proportion of cancer cells in a sample. The probability model can include a continuous model, such as a Gaussian, gamma, or exponential distribution. Discrete models such as binomial and multinomial distributions can be used.

[0062] E. Other modules
[0063] The data analysis application can further comprise a module configured to annotate putative variants. A putative variant is a variant in the coding region, a predicted phenotype caused by the variant, a cross reference to one or more germline mutations or another database of one or more somatic mutations, one or more mutations The interaction between mutations and drugs, one or more observed mutations in clinical trials, one or more diseases, one or more symptoms, or one or more side effects can be annotated.

[0064] The data analysis application can further comprise a module configured to assess the clinical meaning of the variant, chromosomal locus, chromosomal region. In some instances, clinical implications can be assessed on a sample or an individual. For example, an assessment can be used to recommend a method of treatment, method of treatment, course of treatment, predicted outcome, predicted efficacy, or risk.
III. Method
[0065] The methods provided herein can include the use of a computer system or computer readable medium. An example of the method is provided in FIG.

  [0066] The methods provided herein can utilize one or more samples from an individual. One or more sequencing libraries can be prepared from one or more samples. Sequencing libraries can be used in the sequencing process or in data analysis. Sequencing libraries can be prepared by any of the methods disclosed herein. Two or more libraries can be prepared simultaneously or at different times. For example, a sequencing library can be prepared from nucleic acids extracted by tumor biopsy. Sequencing libraries can be prepared, for example, from nucleic acids extracted from a subject's cell-free DNA sample after preparing a sequencing library from a tumor biopsy.

  [0067] Sequencing of the sequencing library can be performed to provide sequencing reads. Sequencing reads can be aligned to a reference genome, eg, to the described reference genome. The reference genome can be a human reference genome, such as a published human genome (eg, hg18, hg19, or hg37).

  [0068] Read alignment with sequencing libraries from one or more samples of a subject can be described by the joint probability and thus can be analyzed simultaneously. In some cases, lead alignments from all available sequencing libraries obtained from the subject's samples (tumor and normal tissue samples, solid tissue and fluid samples, pre- and post-treatment samples) were analyzed simultaneously Ru. In some cases, an analysis from a sequencing library of subjects analyzed in the past is included in the analysis.

  [0069] In some embodiments, the probability that a putative variant at a locus from a sequence library of nucleic acids derived from a subject's tumor sample can be a somatic mutation can be determined. The probability that a putative variant is derived from a tumor or germline nucleic acid (eg, DNA) can be determined, at least in part, by analyzing one or more features described below.

  [0070] Mutation can refer to a change in the nucleotide sequence of the genome as compared to a reference. Mutations may involve large portions of DNA (eg, copy number variation). Mutations may involve the entire chromosome (eg, aneuploidy). Mutations may involve small portions of DNA. Examples of mutations involving small portions of DNA include, for example, point mutations or single nucleotide polymorphisms, polybasic polymorphisms, insertions (eg, insertion of one or more nucleotides at a genetic locus), polybasic changes, Deletions (eg, deletion of one or more nucleotides at a locus) and inversions (eg, inversion of a sequence of one or more nucleotides). The terms "copy number variation" or "CNV" can refer to differences in the copy number of the genetic information. CNV can refer to the copy number differences per genome of a genomic region. For example, in diploid organisms, the expected copy number of an autosomal genomic region is 2 copies per genome. Such genomic regions may be present in two copies per cell. For a recent survey, see Zhang et al. Annu. Rev. Genomics Hum, Genet. 2009. 10: 451-81. CNV can be a source of human genetic diversity and may be associated with complex disorders and diseases, eg, due to changes in gene dosage, gene disruption, or gene fusion. It may also represent a benign polymorphic variant. The CNV can be, for example, as large as more than 1 Mb or as small as, for example, 100 bases to 1 Mb. More than 38,000 CNVs exceeding 100 bases (and less than 3 Mb) have been reported in humans. Together with the SNPs, these CNVs can account for significant amounts of phenotypic variation that vary from individual to individual. In addition to having adverse effects, eg causing disease, advantageous changes can also be brought about. The term "structural polymorphism" can refer to a mutation in the structure of a chromosome. Structural polymorphisms can be deletions, replications, copy number variants, insertions, inversions, and translocations. In some cases, two far apart areas are brought close. For example, a hybrid gene formed from two previously separate genes that can be linked by a translocation, deletion, or inversion event can be referred to as a "gene fusion" or "fusion gene" .

[0071] A. Additional samples from the same subject
[0072] The probability that the putative variant is derived from a tumor or germline nucleic acid, eg DNA, is in part a germline variant at a chromosomal locus in a sample other than a tumor sample from the subject. And / or can be determined by detecting somatic mutations. For example, referring to FIG. 6, it has been found that locus 601 at chromosome A is associated with cancer. On the other hand, variants at locus 611 of chromosome B and locus 612 of chromosome C in non-tumor samples (eg, blood) are signatures of oncogenesis. Thus, evaluation of variants at loci 611 and 612 can be used to calculate the probability that a subject has a genetic mutation of the tumor at locus 601.

  [0073] For example, in some cases, where the patient's germline cells contain a BRCA1 variant, the BRCA1 variant is not from a somatic mutation of a tumor. Other scenarios can be considered in the probabilistic model. For example, one scenario is that a BRCA1 mutation occurred independently in germline and tumor cells. Another scenario is that a BRCAl mutation is present in one cell type and not in another.

[0074] B. Occurrence frequency of variants existing around gene locus
[0075] The probability that a putative variant is derived from a tumor or germline nucleic acid, such as DNA, is, in part, the frequency at which the variant is present in a series of sequence reads that are aligned across loci containing the variant. It can be determined by evaluating. For example, with reference to FIG. 7, it is known that genetic mutations in the tumor occur at locus 701. Variants also occur frequently near locus 701. If the sequence 702 of a given sample contains a locus 701, the evaluation of whether the sample has a genetic mutation in the tumor at 701 is by analyzing the frequency of appearance of one or more variants in the vicinity of the locus 701. It can be assessed. If the frequency of occurrence is high, then the probability of the mutation occurring at locus 701 is high.

  [0076] For example, if a sequencing of a biopsy is performed and a lead containing a known tumor gene mutation is missing, a mutational variant exists by analyzing a sequence lead in the vicinity of the tumor locus Probability can be inferred. If the neighborhood contains more variants, the sample is more likely to contain a tumor mutation.

[0077] C. Error rate of sequencing equipment
[0078] The probability that a putative variant is derived from a tumor or germline nucleic acid, such as DNA, is determined by analyzing the error rate of the sequencing instrument used to generate the sequence read used for read alignment. can do. Errors and / or noise may occur during the process of sample preparation and sequencing. Thus, the error rate reported by the sequencing device can be used to assess whether the putative variant is due to an error.

  [0079] The error rate of the sequencing device can be determined at least in part by the sequence quality score provided with the sequencing read (eg, text that stores both the biosequence and its corresponding quality score) Base format, FastQ score). In some cases, the error rate is adjusted by the calibration information. Such calibration information is determined, for example, by directly detecting a mutation that is most likely due to a sequencing error or a PCR variant, by quantifying the amount of putative variants with low frequency of occurrence. be able to.

[0080] D. Multiple relation
[0081] The probability that a putative variant is derived from a tumor or germline nucleic acid, such as DNA, can be determined by analyzing the multiple relationship of chromosomal segments in the tumor sample. If the chromosome or chromosomal segment has an unexpected replication in the sample, the probability of genetic mutation of the tumor is increased.

  In some cases, estimates of the fold relationship include diploid, haploid, euploid, zygoidy, or polyploid. In some cases, gene replication in a tumor, regional replication, or chromosomal replication can occur, and fold relationships can be inferred by comparison to either control samples or other sequences of the same sample. In addition, other information hidden in the sample can be used, for example, the patient's medical history of the sample, another putative variant associated with a high likelihood putative variant.

[0083] E. Cancer evolution
[0084] The probability that a putative variant is derived from a tumor or germline nucleic acid, such as DNA and RNA, can be determined by analyzing the process of clonal evolution of cancer. In various applications, the first state can be described by a first probability model and the second state can be described by a second probability model. The transition from the first state to the second state can be described by the stochastic process of transforming from the first probability model to the second probability model. Once the evolutionary process of the cancer is characterized by a stochastic process, the observational data in the first state can be used to infer or predict the possible state in the second state.

  [0085] Examples of clonal evolution of cancer that can be considered in the analysis include the time to evolve from one stage of cancer to another, the size of the tumor tissue as it evolves with time, the size of the tumor tissue from the primary organ to another. It includes, but is not limited to, the process of metastasis to distant organs and the process of cancer growth with accompanying symptoms.

[0086] F. Information from other subjects
[0087] The probability that a putative variant is derived from a tumor or germline nucleic acid, such as DNA, can be determined by analyzing base calls at the same locus in samples from different subjects. Subjects from the same family, or from the same race, or from the same population may share similar genetic traits. For example, knowledge of the presence or absence of polymorphisms at loci of a reference population can be modeled as prior probabilities. Thus, genetic information from other subjects can provide additional information to calculate probabilities.

  For example, a particular locus may contain more mutations in the general population, and some loci may exhibit high levels of specificity. The prior probability that loci with high levels of mutations in the general population contain variants is higher than the prior probability that loci showing high levels of purified selection contain variants. The frequency of occurrence of a variant at a particular locus can be determined by past or simultaneous observations, such as the 1000 genome project or published studies.

[0089] G. Recurrent cancer mutation
[0090] The probability that a putative variant is derived from a tumor or germline nucleic acid, such as DNA, can be determined by analyzing knowledge of mutations in recurrent cancer at the locus. Mutations already identified in earlier samples may also occur in later samples. Thus, mutations of recurrent cancer can provide a prior probability model. Such appearance frequency can be determined, for example, by additional observation from cancer patients (eg, from COSMIC or TGCA).

[0091] H. Estimated value of cellularity
[0092] The probability that a putative variant is derived from a tumor or germline nucleic acid, such as DNA, can be determined by analyzing the proportion of cancer cells in the sample. If the sample contains more cancer cells, the probability of the putative mutation being a tumor (somatic) mutation is high. Thus, estimating the proportion of cancer cells can provide additional information in recognizing putative variants.

  [0093] Cellularity can be a fraction of nucleic acids in a sample derived from a tumor. Cellularity can be estimated by examining (eg, visual inspection) a biopsy sample prior to nucleic acid extraction. The examination can be based on visual inspection, imaging, pathological studies, or medical history. Cellularity can be determined by the level of tumor-derived variants in the nucleic acid sample. In some cases, cellularity is a value from 0 to 1 that indicates the probability that a nucleic acid (eg, DNA) molecule from germline is present in a tumor sample.

[0094] I. Correction factor
[0095] The probability that a putative variant is derived from a tumor or germline nucleic acid, such as DNA, is each mutation at a locus, at least in part, from empirical data of another subject's data or from past samples. It can be determined by determining the appearance frequency of the body. In some cases, the correction factor can be used in such a way that variants not previously observed are not assigned a prior probability of occurrence zero. The correction factor can be a Laplace correction. For a method of determining the probability, for example, Cleary et al. Joint Variation and De Novo Mutation Identification on Pedigrees from High-Throughput Sequencing Data, Journal of Computational Biology vol. 21, pp. No. 405-419 (2014), which is incorporated herein by reference in its entirety.
IV. Method of calculation
[0096] An exemplary method of determining the probability that a variant is derived from a tumor or germline DNA is to use a Bayesian network (eg, Koller & Friedman, which is incorporated herein by reference in its entirety). See Probabilistic Graphical Models). FIG. 8 shows a diagram of an exemplary Bayesian network. In the network diagram, “C” represents the inferred variant call, “R” represents the base call of a series of aligned reads across loci, “P” is a multiple relationship at the locus, “U” Represents the cellularity of the sample. The following conditional probability distribution (CPD) can be supplied with appropriate values to infer the probability that the variant is derived from a tumor or germline DNA molecule in each sample. (A) P (R | C), the probability of a series of reads given a specific variant call, (b) P (C _t | C _g ), the primary tumor given the germline at that locus Probability of cells, and (c) P (C _cf | C _t ), probability of tumor call in cf-DNA given call in primary tumor sample.

[0097] Cellularity is described by the Bayesian network variable "U" which can represent cellularity (eg, the probability that a sequencing read is of a value 0 to 1 by cancer cells) be able to. This value can be provided prior to analysis, but in some cases it can be inferred from the data by providing a prior estimate. When considering cellular solidity, the probability of the cellular solid fraction in the tumor given a lead in the tumor and the probability of the cellular solid fraction in the plasma given a lead in the acellular fraction of the plasma Two new CDPs can be deduced: P (U _t | R _t ) and P (U _ct | R _ct ).

  [0098] Combining population calling methods with these methods is described, for example, in Cleary et al. , Journal of Computational Biology, vol. 21, pp. Using the method described in 405-419 2014 but healthy by simultaneously calling a bank of data from other samples while simultaneously calling a germline containing cancer tissue Detection of germline mutations in tissues can be improved.

[0099] CPD P (R | C) is described by Cleary et al. , Journal of Computational Biology, vol. 21, pp. No. 405-419 (2014). The CPD in (b) and (c) above can be determined based on empirical values of somatic mutation rates that can be adjusted for each tumor type and signature of the dominant mutation. In the case of P ( _Ct | _Cg ), and by assuming simple phylogenetic relationships between the primary tumor and the tumor DNA observed in cell-free fluid, we assume simple inheritance of the variants rather than Mendelian segregation. To detect de novo mutations in offspring, see, eg, Cleary et al. , Journal of Computational Biology, vol. 21, pp. Calculations similar to those described in 405-419 (2014) can be used to determine CPD.

[00100] In one example, only primary tumor tissue or cell free DNA is available for analysis. In such cases, using prior information, P (C _t | C _tp ) (C _tp is based on prior observation of a cancer patient) prior to observing a particular somatic mutation allele at that locus Probability), and CPD such as P (G _t | G _p ) (G _t is the genotype of a germline variant present in the tumor given G _p ), and this locus The probability of observing a particular genotype at is derived from a population-wide mutation survey (such as the 1000-man genome project). These probabilities are then provided as a score for each variant analyzed in the output, recalibrated based on empirical verification using machine learning methods if necessary, and then annotated downstream Alternatively, appropriate false positive and / or false negative rates can be determined for a given application, such as a clinical report.
V. Computing system
[00101] The methods, computer systems or computer readable media provided herein may comprise or utilize a processor. The processor can include one or more hardware control processing unit (CPU) processors. The processor can be a desktop computer processor, a server processor, and a mobile processor. The processor can include a microprocessor.

  [00102] A memory module can be used in or with a method, computer system, or computer readable medium provided herein. A memory module can be one or more physical devices used to temporarily or permanently store data or programs. The memory module can be volatile memory and may require power to maintain the stored information. In some cases, the memory module is non-volatile memory and holds stored information when the computing system is not energized. In a further embodiment, the non-volatile memory comprises flash memory. In some embodiments, non-volatile memory includes dynamic random access memory (DRAM). In some embodiments, non-volatile memory includes ferroelectric memory (FRAM). In some embodiments, non-volatile memory includes phase change memory (PRAM).

  [00103] The methods, computer systems, or computer readable media provided herein may comprise or utilize an operating system. The operating system can be, for example, software that includes programs and data that can manage the hardware of the device and provide services for executing applications. Those skilled in the art will appreciate, as non-limiting examples, suitable server operating systems such as FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle®. It will be appreciated that the following may be mentioned: Solaris, Windows Server, and Novell® NetWare. Those skilled in the art will appreciate, as non-limiting examples, suitable personal computer operating systems such as Microsoft® Windows, Apple® Mac OS X®, UNIX®, and GNU / Linux. It will be appreciated that operating systems similar to UNIX such as (registered trademark) may be mentioned. In some embodiments, the operating system is provided by cloud computing. Those skilled in the art will also appreciate, as non-limiting examples of suitable mobile smartphone operating systems, Nokia® Symbian® OS, Apple® iOS®, Research In Motion® Trademarks BlackBerry OS (registered trademark), Google (registered trademark) Android (registered trademark), Microsoft (registered trademark) Windows Phone (registered trademark) OS, Microsoft (registered trademark) Windows Mobile (registered trademark) OS, Linux (registered trademark) It will be appreciated that there may be mentioned Palm), and Palm (R) WebOS (R).

  [00104] Machine readable instructions may include a sequence of CPU executable instructions of a digital processing device written to perform a specified task. In view of the disclosure provided herein, one of ordinary skill in the art will recognize that computer programs can be written in different versions of different languages. In some embodiments, machine readable instructions include one sequence of instructions. In some embodiments, machine readable instructions include multiple sequences of instructions. In some embodiments, machine readable instructions are provided from one location. In another embodiment, machine readable instructions are provided from multiple locations. In various embodiments, machine readable instructions include one or more software modules. In various embodiments, the machine-readable instructions comprise, in part or in whole, one or more web applications, one or more mobile applications, one or more stand-alone applications, one or more web browser plug-ins, Includes extensions, add-ins, add-ons, or a combination of these.

  [00105] A computer readable storage medium can include a memory module. A computer readable storage medium can be a tangible component of a digital processing device. In still other embodiments, the computer readable storage medium is optionally removable from the digital processing device. In some embodiments, computer readable storage media include, by way of non-limiting example, CD-ROM, DVD, flash memory device, solid state memory, magnetic disk drive, magnetic tape drive, optical disk drive, cloud computing systems and services Etc. In some cases, the programs and instructions are permanently, substantially permanently, semi-permanently or non-temporarily encoded on the medium.

  [00106] The present disclosure provides a computer control system programmed to implement the methods of the present disclosure. FIG. 9 shows a computer system 901 programmed or otherwise configured to perform the disclosed sequence analysis. Computer system 901 can be a user electronic device or a computer system remotely located with respect to the electronic device. The electronic device can be a mobile electronic device.

  [00107] Computer system 901 may be a central processing unit (CPU, and as used herein "processor" and "computer processor") 905, which can be a single core or multi-core processor, or multiple processors for parallel processing. Can be included. Computer system 901 may also include a memory or memory location 910 (e.g., random access memory, read only memory, flash memory), electronic storage 915 (e.g., a hard disk), communication interface 920 (for communicating with one or more other systems). For example, peripheral devices 925 may be included such as network adapters), as well as cache, other memory, data storage, and / or electronic display adapters. The memory 910, the storage device 915, the interface 920, and the peripheral device 925 communicate with the CPU 905 through a communication bus (solid line) such as a motherboard. Storage 915 may be a data storage (or data repository) that stores data. Computer system 901 can be operatively coupled to a computer network (“network”) 930 using a communication interface 920. The network 930 can be the Internet, the Internet and / or an extranet, or an intranet and / or an extranet in communication with the Internet. Network 930 is, in some cases, a telecommunications and / or data network. Network 930 can include one or more computer servers that can enable distributed computing, such as cloud computing. Network 930 can implement a peer-to-peer network, which can, in some cases, utilize computer system 901 to enable devices coupled to computer system 901 to act as clients or servers.

  [00108] The CPU 905 can execute a sequence of machine readable instructions that can be embedded in a program or software. The instructions may be stored in a memory location, such as memory 910. The instructions can be directed to CPU 905, which can then be programmed or otherwise configured to implement the disclosed method. Examples of operations performed by CPU 905 can include fetch, decode, execute, and write.

  [00109] The CPU 905 can be part of a circuit such as an integrated circuit. One or more other components of system 101 can be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

  [00110] The storage device 915 can store files such as drivers, libraries, and storage programs. Storage 915 may store user data, such as user selections and user programs. Computer system 901 may optionally include one or more additional data storage devices external to computer system 901, such as located on a remote server in communication with computer system 901 through an intranet or the Internet. it can.

  Computer system 901 can communicate with one or more remote computer systems through network 930. For example, computer system 901 can communicate with a user's remote computer system. Examples of remote computer systems include personal computers (e.g. portable PCs), slate or tablet PCs (e.g. Apple.RTM. IPad, Samsung.RTM. Galaxy Tab), phones, smart phones (e.g. Apple.RTM.) ) IPhone, Android compatible device, Blackberry (registered trademark), or a portable information terminal. A user can access computer system 901 via network 930.

  [00112] A method as described herein utilizes machine (eg, computer processor) executable code stored in a location of electronic storage of computer system 901, eg, in memory 910 or electronic storage 915. Can be realized. Machine executable or machine readable code may be provided in the form of software. During use, the code may be executed by processor 905. In some cases, the code may be retrieved from storage 915 and stored in memory 910 for immediate use by processor 905. In some circumstances, electronic storage 915 may be excluded, and machine executable instructions may be stored in memory 910.

  [00113] The code can be precompiled and configured, or compiled at runtime, for use with a machine having a processor adapted to execute the code. The code can be supplied in a programming language, which can be selected to run the code in precompiled or compiled form.

  [00114] Aspects of the systems and methods provided herein, such as computer system 901, can be implemented in programming. Various aspects of the technology generally "produce" or "produce" in the form of machine (or processor) executable code and / or related data carried or embodied in a type of machine-readable medium. It can be considered as an article. The machine executable code may be stored on an electronic storage device, such as a memory (eg, read only memory, random access memory, flash memory) or a hard disk. A "storage device" type medium can provide non-transitory storage at any time of software programming, computer, processor etc., or their related modules (various semiconductor memories, tape drives, disk drives etc. Can include any and all tangible memory). All or part of the software may optionally communicate through the Internet or various other telecommunication networks. Such communication may allow, for example, loading software from one computer or processor to another, for example, from a management server or host computer, to the computer platform of the application server. Thus, other types of media that can have software elements include light waves, radio waves, and so on, through wired and optical landlines, through various airlinks, and across physical interfaces between local devices. An electromagnetic wave is mentioned. The physical elements that carry such waves, such as wired or wireless links, optical links, etc. can also be considered as media with software. As used herein, unless limited to non-transitory tangible "storage" media, any terms such as processor or machine "readable media" are involved in providing instructions to processor for execution. Refers to the medium.

  Thus, a machine-readable medium, such as computer executable code, can take many forms, including but not limited to tangible storage media, carrier media, or physical transmission media. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer, such as those that can be used to implement the databases shown in the figures. Volatile storage media include dynamic memory such as the main memory of such a computer platform. Tangible transmission media include coaxial cables, copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier wave transmission media can take the form of electrical or electromagnetic signals, or acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Thus, common forms of computer readable media are, for example, floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROM, DVD or DVD-ROM, any other optical media, punches Card, paper tape, any other physical storage medium with hole pattern, RAM, ROM, PROM and EPROM, FLASH + EPROM, any other memory chip or cartridge, carrier wave carrying data or instructions, carrier wave A cable or link or any other medium from which the computer can read the programming code and / or data may be mentioned. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

  [00116] Computer system 901 may include or be in communication with an electronic display 935 comprising a user interface (UI) 940, for example, to provide analysis results. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[00117] The disclosed methods and systems can be implemented using one or more algorithms. The algorithm may be implemented using software as executed by central processing unit 905. The algorithm can include, for example, a Bayesian network or statistical analysis.
VI. Sequencing and High Throughput Sequencing Equipment
[00118] The high throughput sequencing equipment used in, or used with, the methods, computer systems, kits, or computer readable media provided herein is a next generation sequencing (NGS) platform (massively parallel) Can be a platform for sequencing). Sequencing is performed to obtain the identity of at least 10 consecutive nucleotides of the polynucleotide (eg, identity of at least 20, at least 50, at least 100, at least 200, at least 500, or more contiguous nucleotides). It can refer to the method used. NGS technology can be used in large scale parallel fashion (eg, as described in Volkerding et al., Clin Chem 55: 641-658 [2009]; Metzker M Nature Rev 11: 31-46 [2010]). It may involve sequencing clonally amplified DNA templates or single DNA molecules. In addition to high-throughput sequencing information, NGS provides digital quantitative information in that each sequencing read is a countable "sequence tag" that represents an individual cloned DNA template or a single DNA molecule. it can. Sequencing can be target sequencing, exome sequencing, or whole genome sequencing. In some cases, sequencing of cell-free DNA from liquid biopsies is performed. In some cases, sequencing of nucleic acids obtained from circulating tumor cells (CTCs) from liquid biopsies is performed. In some cases, sequencing of nucleic acids obtained from a single normal cell and / or tumor cell is performed.

  [00119] While the automated Sanger method is considered a "first generation" technology, Sanger sequencing, including automated Sanger sequencing, can also be used by the methods provided herein. Using further sequencing methods in the methods described herein, including using nucleic acid imaging techniques under development such as atomic force microscopy (AFM) or transmission electron microscopy (TEM) it can.

  [00120] The high throughput sequencing platform (Next Generation Sequencing Platform) used in, or with, the methods, computer systems, or computer readable media provided herein can be a commercially available platform. Commercially available platforms include, for example, sequencing-by-synthesis, ion semiconductor sequencing, pyrosequencing, reversible dye terminator sequencing, ligation sequencing, single molecule sequencing , Sequencing by hybridization, and nanopore sequencing. Platforms for sequencing by synthesis are available, for example, from Illumina, 454 Life Sciences, Helicos Biosciences, and Qiagen. As the Illumina platform, for example, Solexa platform of Illumina, Genome Analyzer of Illumina etc. can be mentioned, for example, Gudmundsson et al (Nat. Genet. 2009 41: 122 6), Out et al (Hum. Mutat. 2009). 30: 1703-12), and Turner (Nat. Methods 2009 6: 315-6), U.S. Patent Application Publication Nos. US20080160580 and U.S. 20080280695, U.S. Patent Nos. 6,306,597, 7,115,400, And 7, 232, 656. 454 Life Science platforms include, for example, GS Flex and GS Junior, and are described in US Patent No. 7,323,305. Platforms by Helicos Biosciences include the True Single Molecule Sequencing platform. Platforms for ion semiconductor sequencing include, for example, the Ion Torrent Personal Genome Machine (PGM), which is described, for example, in US Pat. No. 7,948,015. Platforms for pyrosequencing include the GS Flex 454 system, as described, for example, in US Pat. Nos. 7,211,390, 7,244,559, and 7,264,929. Platforms and methods for sequencing by ligation include, for example, the SOLiD sequencing platform and are described, for example, in US Pat. No. 5,750,341. Platforms for single molecule sequencing include, for example, the SMRT system from Pacific Bioscience.

  [00121] The high throughput sequencing equipment used in, or used with, the methods, computer systems, or computer readable media provided herein are chemically coupled semiconductor technology with sequencing chemistry. It can be the Ion Torrent sequencing platform, which can translate translated information (A, C, G, T) directly into digital information (0, 1) on a semiconductor chip. While not wishing to be bound by theory, hydrogen ions are released as byproducts when nucleotides are incorporated into strands of DNA by a polymerase. The Ion Torrent platform can detect the release of hydrogen atoms as a change in pH. Changes in pH detected can be used to indicate incorporation of nucleotides. The Ion Torrent platform can include a high density array of microfabricated wells to perform this biochemical process in a massively parallel manner. Each well can hold different library elements, which may be clonally amplified. Below the well there may be an ion sensitive layer and below which there may be an ion sensor. The platform can continuously flood the array from one nucleotide to the next. Nucleotides, such as C, can be added to a DNA template and then incorporated into strands of DNA to release hydrogen ions. The charge from the ion can change the pH of the solution, which can be identified by the Ion Torrent ion sensor. If no nucleotide is incorporated, no voltage change is recorded and the base is not called. If two identical bases are present on the DNA strand, the voltage can be doubled and the chip can record that two identical bases have been called. Direct identification allows the incorporation of nucleotides to be recorded in a few seconds. Preparing a library for the Ion Torrent platform may involve the addition (eg, by ligation) of two discrete adapters at both ends of the DNA fragment.

  [00122] The high throughput sequencing equipment used in, or used with, the methods, computer systems, or computer readable media provided herein comprises cluster amplification of library elements on a flow cell and sequencing by synthesis. It can be an Illumina sequencing platform, which can use an approach. Cluster amplified library elements can be subjected to repeated cycles of single base extension directed to the polymerase. Single base extension may involve the incorporation of reversible terminator dNTPs, each dNTP being labeled with a different removable fluorophore. The terms "label" and "detectable moiety" are used herein to refer to any atom or molecule that can be used to provide a detectable signal, and can be attached to a nucleic acid or protein. Can be used interchangeably. The label can provide a detectable signal by fluorescence, radioactivity, colorimetric, gravimetric, x-ray diffraction or absorption, magnetism, enzyme activity and the like.

  [00123] The reversible terminator dNTPs can be 3 'modified to prevent further extension by the polymerase. After incorporation, incorporated nucleotides can be identified by fluorescence imaging. After fluorescence imaging, the fluorophore can be removed and the 3 'modification can be removed to obtain a 3' hydroxyl group, thereby enabling another cycle of single base extension. Preparing a library for the Illumina platform may involve the addition (eg, by ligation) of two discrete adapters at both ends of the DNA fragment.

[00124] The high throughput sequencing equipment used in, or used with, the methods, computer systems, or computer readable media provided herein can employ sequencing-by-synthesis technology, Helicos True Single It can be a Molecule Sequencing (tSMS) platform. In tSMS technology, poly A adapters can be ligated to the 3 'end of DNA fragments. The adapted fragments can be hybridized to the immobilized polyT oligonucleotide on a tSMS flow cell. Library elements can be immobilized on the flow cell at a density of about 1 million templates / cm ² . The flow cell can then be loaded into the instrument, for example a HeliScopeTM sequencer, and a laser can illuminate the surface of the flow cell to reveal the position of each template. The CCD camera can map the position of the template on the flow cell surface. Library elements can be subjected to repeated cycles of single base extension directed to the polymerase. The sequencing reaction is initiated by the introduction of DNA polymerase and fluorescently labeled nucleotides. The polymerase can incorporate a labeled nucleotide into the primer in the form directed to the template. The polymerase and unincorporated nucleotides can be removed. Templates directed to incorporation of fluorescently labeled nucleotides can be identified by imaging the flow cell surface. After imaging, the cleavage step can remove the fluorescent label and the process can be repeated with other fluorescently labeled nucleotides until the desired read length is achieved. Each nucleotide addition step can be used to collect sequence information.

  [00125] The high throughput sequencing equipment used in, or used with, the methods, computer systems, or computer readable media provided herein (eg, Margulies, M. et al. Nature 437: 376). -454 sequencing platform (Roche) as described in -380 [2005]. 454 sequencing can involve two steps. In the first step, the DNA can be sheared into fragments. Fragments can be blunt ended. An oligonucleotide adapter can be ligated to the end of the fragment. The adapter can serve as a primer for fragment amplification and sequencing. The at least one adapter can include a capture reagent, such as biotin. The fragments can be attached to DNA capture beads, such as streptavidin coated beads. The fragments attached to the beads can be PCR amplified in droplets of an oil-water emulsion, and multiple copies of clonally amplified DNA fragments are provided for each bead. In the second step, the beads can be captured in wells that can be of picoliter size. Pyrosequencing can be performed in parallel for each DNA fragment. Pyrosequencing can detect the release of pyrophosphate (PPi) upon nucleotide incorporation. PPi can be converted to ATP by ATP sulphurylase in the presence of adenosine 5 'phosphosulphate. Luciferase can generate a light signal to be detected by converting luciferin to oxyluciferin using ATP. The detected light signal can be used to identify the incorporated nucleotide.

  [00126] The methods provided herein, high throughput sequencing equipment used in, or used with, a computer system or computer readable medium may utilize SOLiDTM technology (Applied Biosystems) it can. The SOLiD platform can utilize ligation-based sequencing techniques. Preparation of a library for use with the SOLiD platform can include linking an adapter to the 5 'and 3' ends of the fragments to generate a fragment library. Alternatively, the adapter is ligated to the 5 'and 3' ends of the fragment, the fragment is circularized, the circularized fragment is digested to generate an internal adapter, and the adapter is on the 5 'and 3' ends of the resulting fragment An internal adapter can be introduced by attaching and generating a mate pair library. Next, a clonal bead population comprising beads, primers, templates, and PCR components can be prepared in the microreactor. Following PCR, the template can be denatured. The beads can be concentrated into beads with an extension template. The template on selected beads can be subjected to a 3 'modification that allows it to be attached to a glass slide. The sequence can be determined by sequentially performing hybridization and ligation of partially disordered oligonucleotides using a central determined base (or base pair) specified by a specific fluorophore. After the color is recorded, the ligated oligonucleotides can be removed and the process can then be repeated.

  [00127] The high throughput sequencing devices used in, or used with, the methods, computer systems, or computer readable media provided herein are single molecule real time (SMRTTM Sequencing Platform (Pacific Biosciences) In SMRT sequencing, the continuous incorporation of dye-labeled nucleotides can be imaged during DNA synthesis.A single DNA polymerase molecule is linked to a phosphate linked nucleotide on the growth primer strand. Once captured, they can be attached to the bottom surface of individual zero-mode wavelength identifiers (ZMW identifiers) that acquire sequence information.ZMWs diffuse quickly into and out of ZMWs in microseconds. The DNA polymerase against the background can point to a confined structure that allows one to observe the incorporation of a single nucleotide, in contrast, the incorporation of a nucleotide may occur in milliseconds, during which fluorescence The label can be excited to produce a fluorescent signal that can be detected Detection of the fluorescent signal can be used to generate sequence information Next, the fluorophore is removed and the process repeated. The preparation of the library for the SMRT platform can involve the ligation of a hairpin adapter to the end of the DNA fragment.

  [00128] The high throughput sequencing equipment used in, or used with, the methods, computer systems, or computer readable media provided herein (eg, Soni GV and Meller A. Clin Chem 53: 1996 Nanopore sequencing can be used-as described in 2001 [2007]. Nanopore sequencing DNA analysis techniques include those from Oxford Nanopore Technologies (Oxford, United Kingdom). Nanopore sequencing can be a single molecule sequencing technology whereby single molecules of DNA are sequenced directly as they pass through the nanopore. Nanopores can be small holes as small as 1 nanometer in diameter. Immersing the nanopore in a conductive fluid and applying an electrical potential (voltage) across it can result in a slight current flow due to the conduction of ions through the nanopore. The amount of current flow can be sensitive to the size and shape of the nanopore, and to occlusion, for example by DNA molecules. As the DNA molecule passes through the nanopore, each nucleotide on the DNA molecule can block the nanopore to a different extent, and the magnitude of the current passing through the nanopore can be varied to a different extent. Thus, this change in current as the DNA molecule passes through the nanopore can represent the reading of the DNA sequence.

  [00129] The high throughput sequencing equipment used in, or used with, the methods, computer systems, or computer readable media provided herein is described (eg, in US Patent Application Publication 20090026082). Chemical field effect transistors (chemFETs) can be used. In one example of the technology, DNA molecules can be placed in the reaction chamber, and template molecules can be hybridized to a sequencing primer coupled to a polymerase. Incorporation of one or more triphosphates into a new nucleic acid strand at the 3 'end of the sequencing primer can be identified by the change in current with chemFETs. The array can have multiple chemFET sensors. In another example, single nucleic acids can be attached to beads, nucleic acids can be amplified on beads, individual beads can be transferred to individual reaction chambers on a chemFET array, and each chamber Have a chemFET sensor and can sequence nucleic acids.

  [00130] The high-throughput sequencing equipment used in, or used with, the methods, computer systems, or computer readable media provided herein can utilize drop electron microscopy (TEM). . A method called discrete molecular alignment rapid nanotransport (IMPRINT) is to image high molecular weight (150 kb or more) DNA selectively labeled with heavy atom markers by single atomic resolution drop electron microscopy, and these molecules Can be included on the ultrathin film in a very dense (3 nm strand spacing) side-by-side array with constant spacing between bases. Electron microscopy can be used to image molecules on the membrane to determine the position of heavy atom markers and to extract base sequence information from DNA. The method may be further described in PCT patent application publication WO 2009/046445. The method can allow sequencing of the complete human genome in less than 10 minutes.

  [00131] The high throughput sequencing equipment used in, or used with, the methods, computer systems, or computer readable media provided herein can utilize sequencing by hybridization (SBH) . SBH can include contacting a plurality of polynucleotide sequences with a plurality of polynucleotide probes, each of the plurality of polynucleotide probes optionally being tethered to a substrate. The substrate can be a flat surface comprising an array of known nucleotide sequences. The pattern of hybridization to the array can be used to determine the polynucleotide sequence present in the sample. In another embodiment, each probe is anchored to a bead, such as a magnetic bead or the like. Hybridization to beads can be identified and used to identify multiple polynucleotide sequences in a sample.

  [00132] The length of the sequence read may vary depending on the particular sequencing technology utilized. High-throughput sequencing instruments (NGS platforms) can provide sequence reads that range in size from tens to hundreds or even thousands of base pairs. In some embodiments of the methods described herein, the sequence read is approximately or at least 10 bases long, 15 bases long, 20 bases long, 25 bases long, 25 bases long, 30 bases long, 35 bases long, 40 bases long, 45 bases, 50 bases, 55 bases, 60 bases, 65 bases, 70 bases, 75 bases, 75 bases, 80 bases, 80 bases, 85 bases, 90 bases, 95 bases, 100 bases, 110 bases Length: 120 bases, 130, 140 bases, 150 bases, 200 bases, 200 bases, 250 bases, 300 bases, 350 bases, 400 bases, 400 bases, 450 bases, 500 bases, 600 bases, 700 bases Long, 800 bases long, 900 bases long, 1000 bases long, or more than 1000 bases long.

  [00133] The sequencing platform described herein can comprise a solid support immobilized thereon, wherein the surface bound oligonucleotide captures the sequencing library and immobilizes relative to the solid support Make it possible to The surface bound oligonucleotides generally comprise a sequence that is complementary to the adapter sequence of the sequencing library.

[00134] The high throughput sequencing platform can be used to sequence DNA to different depths. The depth of sequencing (eg, DNA sequencing) can refer to the number of times a nucleotide is read during the sequencing process. The coverage of the sequence can indicate the average number of reads representing a given nucleotide in the reconstructed sequence. Physical coverage can be the average number of times a lead is read or measured by a mate pair lead. The depth can be calculated from the original genome length (G), the number of reads (N), and the average read length (L) as N × L / G. In some cases, deep sequencing (> 7-fold) is performed. In some cases, ultra-deep sequencing is performed (> 100 ×). Sequencing depth in the method described herein is at least 1 ×, 2 ×, 5 ×, 7 ×, 10 ×, 20 ×, 50 ×, 75 ×, 100 ×, 250 ×, 500 ×, 1000 × , 5000 times, or 10,000 times.
VII. Subject, sample, and nucleic acid
[00135] A. subject
[00136] The methods provided herein, computer systems, and samples analyzed in computer readable media can be from one or more subjects or individuals. The subject can be a biological entity comprising the expressed genetic material. Biological entities can be, for example, plants, animals or microorganisms, including bacteria, viruses, fungi and protozoa. The subject can be a tissue, cell, or progeny of a biological entity obtained in vivo or cultured in vitro. The subject can be a mammal. The mammal can be human. The human can be male or female. Humans are from 1 day to about 1 year old, about 1 year to about 3 years old, about 3 to 12 years old, about 13 to about 19 years old, about 20 to about 40 years old, about 40 to about 65 years old, Or you can be over 65 years old. Humans may be diagnosed or suspected to be at high risk of disease. The disease may be cancer. Humans may not be diagnosed or suspected to be at high risk of disease.

[00137] B. sample
[00138] One or more samples used in, or used with, the methods, computer systems, and computer readable media provided herein are any material that contains or is assumed to contain a nucleic acid. Can be. The sample can be a biological sample obtained from a subject. In some embodiments, the biological sample is a liquid sample. The fluid sample can be whole blood, plasma, serum, ascites, cerebrospinal fluid, sweat, urine, tears, saliva, oral cavity samples, cavity rinse, or organ rinse. The fluid sample may be an essentially acellular fluid sample or may contain cell free nucleic acid (eg, plasma, serum, sweat, plasma, urine, sweat, tears, saliva, sputum, cerebrospinal fluid) . In another embodiment, the biological sample is a solid biological sample, such as feces or tissue biopsy. The sample can also include in vitro cell culture components (including, but not limited to, prepared media obtained by growing cells in cell culture media, recombinant cells, and cellular components). The sample can comprise single cells, such as cancer cells, circulating tumor cells, cancer hepatocytes, and the like. The sample can contain multiple cells. In some cases, the sample is approximately or at least 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65% %, 70%, 75%, 80%, 85%, 90%, 95%, 99%, or 100% tumor cells. The subject may be suspected or known to harbor a solid tumor, or may be a subject who previously harbored a solid tumor.

[00139] In some cases, both a tumor sample from the subject and normal cells are obtained from the subject.
[00140] In some embodiments, nucleic acids comprising germline sequences are extracted from a subject's biological sample. In some embodiments, the biological sample is a solid tissue. The biological sample can be tissue, such as healthy tissue from a subject. The biological sample can be, for example, a fluid sample such as blood, buffy coat from blood (which can include lymphocytes), saliva, or plasma.

  [00141] In some embodiments, nucleic acids comprising somatic variants are extracted from a biological sample of a subject. In some embodiments, the biological sample is a solid tissue. The solid tissue can be, for example, a primary tumor, a metastatic tumor, a polyp, or an adenoma. In some embodiments, the biological sample is a liquid sample, such as, for example, urine, saliva, cerebrospinal fluid, plasma, or serum. In some cases, the fluid is a cell free fluid. In some cases, cells containing circulating tumor cells are concentrated or isolated from the fluid. In some cases, the sample comprises cell free nucleic acid, eg, DNA.

In some cases, a sample of a tumor is taken at a first time point and sequenced, and another sample of a tumor is taken at a later time point, and the tumor is sequenced.
[00143] C. cancer
[00144] The computing systems, software media, methods, and kits provided herein can utilize tumor samples. Tumor compositions (primary tumors, metastatic tumors) can comprise one or more DNA molecules associated with cancer.

  [00145] The computing systems, software media, methods, and kits provided herein can include estimating the ratio of tumor cells / nucleic acids in a sample.

  [00146] The computing systems, software media, methods, and kits provided herein can include samples collected simultaneously or at different times (simultaneously, one or more samples contain at least two samples) And at least two samples are collected at different times).

  [00147] The computing systems, software media, methods, and kits provided herein can include using different types of cells (eg, lymphocytes, blood cells, tumor cells).

  [00148] The computing systems, software media, methods, and kits provided herein improve monitoring and treatment of a subject with a disease. Diseases include cancer, for example, tumors, leukemia (acute leukemia, acute T cell leukemia, acute lymphocytic leukemia, acute myelocytic leukemia, myeloblastic leukemia, promyelocytic leukemia, myelomonocytic leukemia, monocytes Leukemia, erythroleukemia, chronic leukemia, chronic myelocytic (granulocytic) leukemia, or chronic lymphocytic leukemia, euthymia, lymphoma (Hodgkin's lymphoma, follicular lymphoma, or non-Hodgkin's lymphoma), multiple myeloma , Waldenstrom macroglobulinemia, heavy chain disease, solid tumor, sarcoma, carcinoma (for example, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteosarcoma, lymphangiosarcoma, mesothelioma, Ewing tumor , Leiomyosarcoma, rhabdomyosarcoma), colon cancer, colon cancer, pancreatic cancer, breast cancer, ovarian cancer, prostate cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland , Sebaceous gland cancer, nipple Cancer, papillary adenocarcinoma, cystic adenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, liver cancer, cholangiocarcinoma, choriocarcinoma, seminoma, cancer of gestational period, Wilms tumor, cervical cancer, uterine cancer, testicular cancer, lung cancer, small cell lung cancer, bladder cancer, epithelial cancer, glioma, cranial laryngoma, ventricular epithelioma, pineal tumor, hemangioblast It can be a tumor, an acoustic neuroma, an oligohepatic glioma, a meningioma, a melanoma, a neuroblastoma, a retinoblastoma, an endometrial cancer, a non-small cell lung cancer.

[00149] D. Nucleic acid
[00150] The nucleic acids used in or with the methods, computer systems, computer readable media, and kits provided herein may be RNA, DNA, such as genomic DNA, mitochondrial DNA, viral DNA, synthetic DNA Or cDNA reverse transcribed from RNA.

  [00151] The terms "polynucleotide", "nucleic acid", and "oligonucleotide" can be used interchangeably. They can refer to polymeric forms of nucleotides of any length, either deoxyribonucleotides or ribonucleotides, or their analogs. A polynucleotide can have any three-dimensional structure, and can perform any function known or unknown. The following are coding or noncoding regions of genes or gene fragments, loci defined from linkage analysis, exons, introns, messenger RNA (mRNA), transcription RNA, ribosomal RNA, ribozyme, cDNA, recombinant polynucleotide, Nonlimiting examples of polynucleotides such as branched polynucleotides, plasmids, vectors, isolated DNA of any sequence, isolated RNA of any sequence, nucleic acid probes, and primers. Polynucleotides can include modified nucleotides, such as methylated nucleotides and nucleotide analogs. If present, modifications to the nucleotide structure can be imparted before or after assembling the macromolecule. The sequence of nucleotides may be interrupted by non-nucleotide components. The polynucleotide can be further modified after polymerization, such as by conjugation with a labeling component.

[00152] The terms "target polynucleotide", "target region" or "target" as used herein may refer to the polynucleotide of interest under study. In certain embodiments, the target polynucleotide comprises one or more sequences of interest under investigation. The target polynucleotide can, for example, comprise a genomic sequence. The target polynucleotide can include a target sequence whose presence, amount, and / or nucleotide sequence, or for which it is desirable to determine changes thereof.
VIII. Nucleic acid library generation
[00153] The methods, computer systems, computer readable media, and kits provided herein can utilize a nucleic acid library. Provided herein are methods, compositions, and kits for nucleic acid library formation. Library formation can include target capture via probe hybridization and extension prior to sequencing. Paired end leads can be used to align the leads from a given probe. The process of preparing the library can include the generation of fragmented DNA, compatible DNA, target capture, surface loading, and sequencing, with adapters at each end of the fragment of DNA between compatible DNA and target capture generation. The fragments are amplified and not amplified by amplification with primers.

  [00154] The nucleic acid sample can be used to prepare a nucleic acid library for sequencing. Preparation of nucleic acid libraries can include any method as known in the art or as described herein. Nucleic acid sequencing libraries can be formed by target enrichment, using, for example, target specific primers. In some cases, nucleic acid libraries are not based on target specific approaches. FIG. 10 shows an exemplary workflow for DNA preparation and library generation. The total preparation time may be about 8 hours. The preparation can include enzymatic manipulations that are interspersed by incubation with Solid Phase Reverse Immobilization (SPRI) beads to purify nucleic acid intermediates. Preparation of nucleic acid (e.g. DNA) libraries may involve preparation of nucleic acids (e.g. DNA) including: a) repair of nucleic acids (e.g. DNA) b) nucleic acids (e.g. DNA) Phosphorylation reactions, and / or c) capping of nucleic acids (eg, DNA) can be included. Generation of nucleic acid libraries can include the addition (eg, ligation) of adapters to nucleic acids, "capture" (eg, annealing target specific primers to nucleic acids), extension, and / or amplification. The nucleic acid library can be a single stranded nucleic acid library or a double stranded nucleic acid library. The nucleic acid library can be a DNA library. In some embodiments, the nucleic acid library is a ssDNA library. In some embodiments, the nucleic acid library is a partial ssDNA library.

[00155] A. Nucleic acid repair and fragmentation
[00156] The nucleic acids can be repaired prior to forming the nucleic acid library. For example, nucleic acids (eg, DNA) from a sample (eg, any sample described herein, eg, formalin fixed paraffin embedded (FFPE) sample) can be used for preparation of a library, eg, a sample , FFPE samples) (eg, DNA) can include mutations such as oxoguanine, dUTP, bridging moieties, and / or abasic sites. In some cases, damaged bases are removed (eg, excised) from the DNA sample. In some cases, no "correction" processing step is involved (base errors are not corrected). In some cases, the nucleic acid in the sample does not contain a mutation.

  [00157] In some cases, the nucleic acids in the library are fragmented. The fragments used in preparation of the library are about 50 to about 500 bases / bp, about 100 to about 500 bases / bp, about 100 to about 400 bases / bp, about 100 to about 300 bases / bp, about 100 to about 300 It can have an average size of 200 bases / bp, about 200 to about 500 bases / bp, about 200 to about 400 bases / bp, or about 200 to about 300 bases / bp.

  [00158] DNA, eg, fragmented DNA, is treated with a base excision repair enzyme (eg, Endo VIII, formamide pyrimidine DNA glycosylase (FPG)) to excise damaged bases that may interfere with polymerization. Can. The DNA can then be treated with a proofreading polymerase (eg, T4 DNA polymerase) to polish the ends and replace the damaged nucleotide (eg, an abasic site). In some embodiments, the DNA is not treated with a proofreading polymerase to polish the ends and replace damaged nucleotides.

[00159] B. Nucleic acid processing
[00160] Fragmentation of nucleic acids (eg, DNA) can be phosphorylated (eg, using a kinase) and capped with ddNTPs. In some cases, the 5 'end of the nucleic acid is phosphorylated.

[00161] C. Adapter addition
[00162] Single stranded adapters can be ligated to single stranded DNA fragments from a sample. Two orders of magnitude yield of adapted DNA fragments can be achieved to enable improved recovery of sequence information from the sample. The adapter can be added to the nucleic acid, for example via a primer or by a ligation reaction. Adapters, such as ssDNA adapters, can be added, eg, ligated, to the 5 'end of ssDNA, the 3' end of ssDNA, or both the 5 'and 3' ends of ssDNA. The 5 'end of the nucleic acid fragment and / or the adapter can, for example, be adenylated prior to the ligation reaction. The yield of adapted DNA may be two orders of magnitude.

  [00163] The fragments can be modified with an adapter sequence, which can affect the binding (eg, capture and / or immobilization) of the fragments to the sequencing platform. The adapter sequence can include a defined oligonucleotide sequence that affects the binding of library elements to the sequencing platform. The adapter is at least 25%, 50%, 60%, 70%, 80%, 90%, or 100% of the oligonucleotide sequence immobilized on the solid support (eg, sequencing flow cell or beads) The sequences can be complementary or identical. The adapter sequence can comprise a defined oligonucleotide sequence that is at least 50%, 60%, 70%, 80%, 90%, or 100% complementary or identical to the sequencing primer. Sequencing primers can allow nucleotide incorporation by the polymerase, and nucleotide incorporation is monitored to provide sequencing information. The sequencing primer can be about 15 to about 25 bases. The adapter is a sequence that is at least 25%, 50%, 60%, 70%, 80%, 90%, or 100% complementary or identical to the oligonucleotide sequence immobilized on the solid support, as well as the sequence It can comprise a sequence that is at least 70% complementary or identical to a single primer. Bonding can also be achieved by suturing the adapters in series. The number of adapters that can be sutured can be one, two, three, four or more. The sutured adapter can be at least 35 bases, 70 bases, 105 bases, 140 bases or more.

  [00164] The adapter can include a barcode sequence. The term "barcode sequence" can refer to a unique sequence of nucleotides that can encode information about the assay. The barcode sequence can encode information on the identity of the discriminating allele, the identity of the target polynucleotide or genomic locus, the identity of the sample, the subject, the molecule, or any combination thereof. The barcode sequence can be part of a primer, a reporter probe, or both. The barcode sequence can be at the 5 'end or 3' end of the oligonucleotide, or can be located in any region of the oligonucleotide. The barcode sequence may or may not be part of the template sequence. Barcode sequences may vary widely in size and composition, and reference later will guide the selection of the set of barcode sequences appropriate for a particular embodiment. Brenner, U.S. Patent No. 5,635,400, Brenner et al, Proc. Natl. Acad. Sci. 97: 1665-1670 (2000); Shoemaker et al, Nature genetics, 14: 450-456 (1996); Morris et al, European Patent Application Publication No. 0 798 9 897 A1; Wallace, U.S. Patent No. 5,981,179. The barcode sequence can have a length of about 4-36 nucleotides, about 6-30 nucleotides, or about 8-20 nucleotides.

  [00165] At least 50%, 60%, 70%, 80%, 90%, or 100% of the sequencing library elements in the library can comprise the same adapter sequence. At least 50%, 60%, 70%, 80%, 90% or 100% of the ssDNA library elements can comprise an adapter sequence at the first end rather than the second end. In some embodiments, the first end is a 5 'end. In some embodiments, the first end is a 3% end. The adapter sequence is chosen by the user according to the sequencing platform used for sequencing. By way of example only, sequencing of Illumina by synthetic platforms can include a solid support on which the first and second populations of surface-bound oligonucleotides are immobilized. Such oligonucleotides can comprise sequences that hybridize to the first and second Illumina specific adapter nucleotides and prime the extension reaction. Thus, the DNA library element can comprise a first Illumina-specific adapter that is partially or totally complementary to the first population of surface-bound oligonucleotides of the Illumina system. As just another example, the SOLiD system, and the Ion Torrent, GS FLEX system can include a solid support in the form of a single population of surface-bound oligonucleotides immobilized beads. Thus, in some embodiments, the ssDNA library element comprises an adapter sequence that is complementary to the surface binding oligonucleotides of the SOLiD system, the Ion Torrent system, or the GS Flex system.

[00166] D. Elongation
[00167] Extension products may be generated from nucleic acid fragments. The extension product can be generated by annealing the primer to the adapter sequence on the 3 'end of the nucleic acid and extending the primer. Such extension products are not target specific. The extension product may be generated by annealing the primer to a target specific sequence within the ss nucleic acid (eg, ssDNA) containing an adapter at the 5 'end and / or the 3' end and extending the primer. it can. Such extension products can be target specific extension products. A plurality of target specific primers (eg, target specific sequences of about 20 to about 35 bases) can be used to create a library. Target specific primers can include an adapter sequence, for example at the 5 'end.

[00168] E. amplification
[00169] In some cases, whole genome PCR is not performed, which can minimize expression bias. In some cases, amplification is performed on extension products in solution. In some cases, multiple rounds of amplification are performed on extension products in solution prior to sequencing.

[00170] F. Preparation of ssDNA fragment / ssDNA library (3 'end adapter)
[00171] Provided herein are methods, compositions, and kits for generating ssDNA libraries, for example by adding an adapter to the 3 'end of the nucleic acid fragment. Single stranded nucleic acid libraries can be prepared from double stranded nucleic acid or single stranded nucleic acid samples using any means known in the art or described herein.

[00172] Sample
[00173] The starting sample can be a biological sample obtained from a subject. Exemplary subjects and biological samples are described herein. The sample can be a solid biological sample, such as a tumor sample. Solid biological samples can be processed. Treatment can include, for example, fixation in formalin solution followed by embedding in paraffin (eg, is an FFPE sample). Processing can include freezing. In some cases, the sample is neither fixed nor frozen. Samples that are not fixed and not frozen can be stored in a storage solution configured to store nucleic acids. Exemplary storage solutions are described herein. In some embodiments, non-nucleic acid material can be removed from the starting material using, for example, enzymatic treatment (eg, using a protease). The sample can be subjected to homogenization, sonication, French press, dance, freeze / thaw, and subsequent centrifugation. Centrifugation can separate the nucleic acid containing fraction from the nucleic acid free fraction. In some cases, the sample is a liquid biological sample. Exemplary liquid biological samples are described herein. The liquid biological sample can be a blood sample (eg whole blood, plasma or serum). Whole blood samples are described, for example, in Fuss et al., Which is incorporated herein by reference. Curr Protoc Immunol (2009) Chapter 7: By using the Ficoll reagent, which is described in detail in Unit 7.1, it can be given to acellular components (eg, plasma, serum) and cellular components.

  [00174] Nucleic acids can be isolated from biological samples using any means known in the art. For example, nucleic acids can be extracted from biological samples using liquid extraction (eg, Trizol, DNAzol) techniques. Nucleic acids can also be extracted using commercially available kits (eg, Qiagen DNeasy kit, QIAamp kit, Qiagen Midi kit, QIAprep spin kit).

  [00175] Nucleic acids can be condensed by known methods, including by way of example only centrifugation. The nucleic acids can be attached to a selective membrane (eg, silica) for purification purposes. The nucleic acids can also be enriched for fragments of a desired length, such as fragments less than 1000, 500, 400, 300, 200, or 100 base pairs in length. Such size-based enrichment can be performed, for example, using PEG induced precipitation, electrophoresis gel, or chromatography material (Huber et al. (1993) Nucleic Acids Res. 21: 1061-6), gel filtration chromatography, TSK gel (Kato et al. (1984) J. Biochem, 95: 83-86), which documents are incorporated herein by reference.

[00176] Polynucleotides extracted from biological samples can be selectively precipitated or condensed using any method known in the art.
[00177] Nucleic acid samples can be enriched for target polynucleotides. Target enrichment can be by any means known in the art. For example, nucleic acid samples can be enriched by amplifying the target sequence using target specific primers. Target amplification can occur in digital PCR format using any method or system known in the art. The nucleic acid sample can be enriched by capturing the target sequence onto an array immobilized with target selective oligonucleotides. The nucleic acid sample can be concentrated by freely hybridizing the target selective oligonucleotide in solution or on a solid support. The oligonucleotide can include a capture moiety that allows capture by a capture reagent. Exemplary capture moieties and capture reagents are described herein. In some cases, the nucleic acid sample is not enriched for target polynucleotides, and, for example, represents the entire genome. In some cases, whole genome amplification is performed.

  [00178] The single stranded nucleic acid library can be a single stranded DNA library (ssDNA library) or an RNA library. The method of preparing the ssDNA library includes denatured double stranded DNA fragments into ssDNA fragments, ligating a primer sequence on one end of the ssDNA fragments, and hybridizing the primers into a primer docking sequence it can. The primer can comprise at least a portion of an adapter sequence that binds to a next generation sequencing platform. The method may further comprise extending the hybridized primers to create a duplex, the duplex comprising the original ssDNA fragment and the extension primer strand. The extension primer strand can be separated from the original ssDNA fragment. The extension primer strand can be collected, the extension primer strand being an element of the ssDNA library. The method of preparing the RNA library can include ligating a primer docking sequence onto one end of the RNA fragment, hybridizing the primers to form a primer docking sequence. The primer can comprise at least a portion of an adapter sequence that binds to a next generation sequencing platform. The method may further comprise extending the hybridized primers to create a duplex, wherein the duplex comprises the original RNA fragment and the extension primer strand. The extension primer strand can be separated from the original RNA fragment. The extension primer strand can be collected, and the extension primer strand is an element of the RNA library.

  [00179] dsDNA can be fragmented by any means known in the art or described herein. The dsDNA may be by physical means, for example by mechanical shear, by atomization or by sonication, by chemical means such as treatment with Fe (II) -EDTA chelate, or by multiple cleaving enzymes, restriction enzymes or fragments. It can be fragmented by enzymatic means such as Tase (NEB).

[00180] In some embodiments, cDNA is generated from RNA that generates random sized cDNA using random primed reverse transcription (RNase H +).
[00181] Fragment size
[00182] The nucleic acid fragment (eg, dsDNA fragment, RNA, or random size cDNA) is less than 1000 bp, less than 800 bp, less than 700 bp, less than 600 bp, less than 500 bp, less than 400 bp, less than 300 bp, less than 200 bp, or less than 100 bp Can. The DNA fragment is about 40 to 100 bp, about 50 to 125 bp, about 100 to 200 bp, about 150 to 400 bp, about 300 to 500 bp, about 100 to 500 bp, about 400 to 700 bp, about 500 to 800 bp, about 700 to 900 bp, about It can be 800-1000 bp, or about 100-1000 bp.

[00183] Repair
[00184] The ends of ds DNA fragments can be polished (eg, blunt ended). The ends of the DNA fragments can be polished by treatment with a polymerase. Polishing can involve the removal of 3 'overhangs, replenishment of 5' overhangs, or a combination thereof. The polymerase can be a proofreading polymerase (eg, 3 'to 5' exonuclease activity). The proofreading polymerase can be, for example, T4 DNA polymerase, Pol 1 Klenow fragment, or Pfu polymerase. Polishing can include removing damaged nucleotides (eg, abasic sites) using any means known in the art.

[00185] Adapter
[00186] Ligation of the adapter to the 3 'end of the nucleic acid fragment can include forming a bond between the 3' OH group of the fragment and the 5 'phosphate of the adapter. Thus, by removing the 5 'phosphate from the nucleic acid fragment, the aberrant ligation of the two library elements can be minimized. Thus, in some embodiments, the 5 'phosphate is removed from the nucleic acid fragment. In some embodiments, the 5 'phosphate is at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% of the nucleic acid fragments in the sample. Or removed from over 95%. In some embodiments, substantially all phosphate groups are removed from the nucleic acid fragment. In some embodiments, substantially all of the phosphate is at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% of the nucleic acid fragments in the sample. Removed from over 95%, or 89%. Removal of phosphate groups from nucleic acid samples may be by any means known in the art. Removal of the phosphate group can include treating the sample with heat labile phosphatase. In some embodiments, the phosphate group is not removed from the nucleic acid sample. In some embodiments, ligation of the adapter to the 5 'end of the nucleic acid fragment is performed.

[00187] Degeneration
[00188] ssDNA can be prepared from single-stranded ds DNA fragments prepared from any means known in the art or described herein. Denaturation of dsDNA can be by any means known in the art, including heat denaturation, incubation in basic pH, denaturation with urea or formaldehyde.

  [00189] The heat denaturation is performed at about 60 ° C., about 65 ° C., about 70 ° C., about 75 ° C., about 80 ° C., about 85 ° C., about 90 ° C., about 95 ° C., or about 98 ° C. This can be achieved by heating the dsDNA sample to above ° C. The dsDNA sample can be heated by any means known in the art, including, for example, incubation in a water bath, a temperature controlled heat block, a thermal cycler. In some embodiments, the sample is heated for 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 minutes.

  [00190] Incubation denaturation at basic pH can be achieved, for example, by incubating ds DNA samples in a solution containing sodium hydroxide (NaOH) or potassium hydroxide (KOH). The solution is approximately 1 mM NaOH, approximately 2 mM NaOH, approximately 5 mM NaOH, approximately 10 mM NaOH, approximately 20 mM NaOH, approximately 40 mM NaOH, approximately 60 mM NaOH, approximately 80 mM NaOH, approximately 100 mM NaOH, approximately 0.2 M NaOH, and approximately 0.3 M NaOH. , NaOH about 0.4 M, NaOH about 0.5 M, NaOH about 0.6 mM, NaOH about 0.7 mM, NaOH about 0.8 mM, NaOH about 0.9 mM, NaOH about 1.0 M, or NaOH about 1.0 M or more Can be included. The solution is approximately 1 mM KOH, approximately 2 mM KOH, approximately 5 mM KOH, approximately 10 mM KOH, approximately 20 mM KOH, approximately 40 mM KOH, approximately 60 mM KOH, approximately 80 mM KOH, approximately 100 mM KOH, approximately 0.2 M KOH, approximately 0.5 M KOH. , About 1 M KOH, or more than about 1 M KOH. In some embodiments, the dsDNA sample is for 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 60 minutes, or 60 minutes. Incubate in NaOH or KOH for more than a minute. The dsDNA can be neutralized with sodium or ammonium salt of acetic acid or with NaOH or KOH incubation followed by acetic acid to neutralize the alkaline solution.

  [00191] Compounds such as urea and formamide contain functional groups capable of forming H bonds with the electronegative center of the nucleotide base. In the case of denaturants at high concentrations (eg, urea 8M or formamide 70%), H-bond competition favors the interaction between the denaturant and the N base over the interaction between the complementary bases, whereby the two The strands can be separated. The term "separation" can refer to physical separation of two elements (e.g., by cleavage, hydrolysis, or degradation of one of the two elements).

[00192] Ligation of Adapters to the 3 'End of Nucleic Acid Fragments
[00193] Adapters can be ligated onto one or both ends of nucleic acid fragments (eg, ssDNA, DNA, RNA). Adapters can be ligated onto the 5 'end and / or the 3' end. In some cases, the adapter is ligated onto the 3 'end of the nucleic acid fragment.

  [00194] The adapter can include a sequence that acts as a template for annealing the primers. The sequence of adapters is at least 70%, 80%, 90%, or 100% complementary to a portion or all of the adapter sequences (NGS adapters, eg, flow cell sequences) that bind to the NGS (large scale parallel sequencing) platform Sequences can be included. The adapter comprises a sequence complementary or identical to at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20 or more consecutive nucleotides of the NGS adapter be able to. In some cases, the adapter does not include a sequence that is complementary or identical to part or all of the NGS adapter (eg, flow cell sequence).

  [00195] The adapter can be adenylated at the 5 'end. The adapter can be conjugated to a capture moiety, which can form a complex with the capture reagent. The capture moiety can be conjugated to the adapter oligonucleotide by any means known in the art. Capture moiety / capture reagent pairs are known in the art. In some cases, the capture reagent is avidin, streptavidin or neutravidin and the capture moiety is biotin. In another case, the capture moiety / capture reagent pair is digoxigenin / wheat germ cell agglutinin.

  [00196] In some cases, the adapter is linked to a nucleic acid fragment. Ligation of the adapter to the nucleic acid fragment is provided by an ATP dependent ligase. The ATP dependent ligase can be an RNA ligase. The RNA ligase can be an ATP dependent ligase. The RNA ligase can be Rnl1 or Rnl2 family ligase. The Rnl1 family ligase can repair single strand breaks of tRNA. Exemplary Rnl 1 family ligases include, for example, T4 RNA ligase, thermostable RNA ligase 1 from Thermus scitoductus bacteriophage TS2126 (Serck ligase), or Circ ligase II. These ligases can catalyze the ATP-dependent formation of phosphodiester bonds between the nucleotide 3-OH nucleophile and the 5 'phosphate group. The Rnl2 family ligase can seal the nick of double stranded RNA. Exemplary Rnl2 family ligases include, for example, T4 RNA ligase 2. The RNA ligase can be an archaebacterial RNA ligase, for example, an archaebacteria RNA ligase from the thermophilic archaebacteria Methanobacterium thermoautotropicum (MthRnl).

  [00197] Ligation of the adapter to the single stranded nucleic acid fragment can include preparing a reaction mixture comprising the nucleic acid fragment, the adapter, and the ligase. The reaction mixture can be heated to effect ligation of the adapter oligonucleotide to the ssDNA fragment. The reaction mixture can be heated to about 50 ° C., about 55 ° C., about 60 ° C., about 65 ° C., about 70 ° C., or more than about 70 ° C. The reaction mixture can be heated to about 60-70 ° C. The reaction mixture can be heated for a time sufficient to effect ligation of the adapter to the nucleic acid fragment. The reaction mixture is about 5 minutes, about 10 minutes, about 15 minutes, about 20 minutes, about 25 minutes, about 30 minutes, about 35 minutes, about 40 minutes, about 45 minutes, about 50 minutes, about 55 minutes, about 60 minutes. The heating can be performed for more than about 70 minutes, about 80 minutes, about 90 minutes, about 120 minutes, about 150 minutes, about 180 minutes, about 210 minutes, about 240 minutes, or about 240 minutes.

  [00198] The adapter can be present in the reaction mixture at a higher concentration than the concentration of nucleic acid fragments in the mixture. In some embodiments, the adapter is at least 10%, 20%, 30%, 40%, 60%, 60%, 70%, 80%, 90%, 100%, or more than the concentration of the nucleic acid fragment in the mixture. Alternatively, it can be present in the reaction mixture at high concentrations of more than 100%. The adapter can be present in the reaction mixture at a concentration at least 10, 100, 1000 or 10000 times higher than the concentration of the nucleic acid fragments in the mixture. The adapter can be present at a final concentration of at least 0.1 μM, at least 0.5 μM, at least 1 μM, at least 10 μM, or more. The ligase can be present in the reaction mixture in a saturating amount.

  [00199] In addition, the reaction mixture can include high molecular weight inert molecules, such as MW 4000, 6000, or 8000 PEG. Inactive molecules are about 0.5%, 1%, 2%, 3%, 4%, 5%, 7.5%, 10%, 12.5%, 15%, 17.5%, 20%, It can be present in an amount of 25%, 30%, 35%, 40%, 45%, 50%, or more than 50% weight / volume. In some embodiments, the inert molecule is about 0.5-2%, about 1-5%, about 2-15%, about 10-20%, about 15-30%, about 20-50%, and the like. Or may be present in an amount of more than 50% weight / volume.

  [00200] After a sufficient time to effect ligation of the adapter to the nucleic acid molecule (eg, ssDNA fragment), unreacted adapter, eg, filtration by molecular weight cutoff, size exclusion chromatography, use of spin columns, polyethylene glycol Selective precipitation with (PEG), selective precipitation with PEG on silica or carboxylate, alcohol precipitation, sodium acetate precipitation, PEG and salt precipitation, or high stringency washing, etc. known in the art It can be removed by any means.

  [00201] In some cases, linked nucleic acid fragments can be captured. Capture of ligated nucleic acid fragments can be performed prior to, or subsequent to, elongation. The ligated nucleic acid fragments can be captured on a solid support. The capture may involve the formation of a complex that includes the capture moiety conjugated to the adapter and capture reagent. The capture reagent can be immobilized on a solid support. The solid support can include extra capture reagent as compared to the amount of coupled diffusion that includes the capture moiety. The solid support can comprise five, ten, or one hundred times more available binding sites of the total number of linked diffusive fragments including the capture moiety.

  [00202] In some cases, for example, when a single stranded adapter is ligated to the 3 'end of a single stranded fragment (eg, a ssDNA fragment), a primer (eg, an adapter specific primer) may be inserted through the adapter. , Are hybridized to the ligated nucleic acid fragments. The primers (e.g., adapter specific primers) can include a 3 'sequence that anneals to the adapter at the 3' end of the single stranded fragment.

  [00203] The primer (eg, an adapter specific primer) can include a portion or all of the NGS adapter sequence, eg, at its 5 'end. Exemplary NGS adapter sequences are described herein. The hybridized primers can be extended to create a duplex comprising the original nucleic acid fragment and the extension primer, the extension primer comprising the original nucleic acid fragment and the reverse complement of the NGS adapter sequence at one end. . Exemplary NGS adapter sequences are described herein. In some embodiments, the NGS adapter sequence in the primer is a sequence that is at least 70%, 80%, 90%, or 100% identical to the surface-bound oligonucleotide (eg, flow cell sequence) of the NGS platform. Including. The NGS adapter sequence comprises a sequence that is at least 70%, 80%, 90%, or 100% complementary to the surface bound oligonucleotide (eg, flow cell sequence) of the NGS platform. The NGS adapter sequence can comprise a sequence that is at least 70%, 80%, 90%, or 100% identical to the sequencing primer used by the NGS platform. The NGS adapter sequence can comprise a sequence that is at least 70%, 80%, 90%, or 100% complementary to the sequencing primers used by the NGS platform. The extension of the adapter primer can be influenced by the thermophilic or thermophilic DNA during calibration. The polymerase is 5'-3 'exonucleotide degradability / inside nucleotide degradation (DNA polymerase I, II, III), or 3'-5' exonucleotide degradation (family A or B DNA polymerase, DNA polymerase I, T4 DNA polymerase can be thermophilic polymerase with activity. In some cases, the polymerase can have exonuclease activity (Taq). The polymerase can provide linear amplification of the immobilized ligation fragment to create multiple copies of the reverse complement of the immobilized ligation fragment. In some cases, the reverse complement strand is made only one copy of the reverse complement strand. In some embodiments, extension primer molecules are separated from the original nucleic acid template (eg, by denaturation, etc., as described herein). The extension primer molecule is free in solution and the original nucleic acid template remains immobilized to the solid support. Extension primer molecules can be harvested to prepare a nucleic acid library where the library element comprises an NGS adapter. At least 50%, 60%, 70%, 80%, 90%, 90%, or substantially all of the library elements can include the NGS adapter.

  [00204] An exemplary method of preparing a nucleic acid library from nucleic acids (eg, DNA or RNA) isolated from biological samples (eg, blood, plasma, urine, stool, mucosal samples) is provided below. The resulting nucleic acid can be fragmented into fragments of about 100 to about 1000, such as about 100 to about 500 bp by enzymatic or mechanical means. Nucleic acids can be fragmented in situ. Nucleic acids can be fragmented from formalin fixed paraffin enveloped (FFPE) tissues or circulating DNA. Nucleic acids can be isolated from FFPE and circulated by the kit (Qiagen, Covaris). The nucleic acid can be DNA. The DNA can be cDNA generated from RNA isolated from a biological sample from the same sample using random primed reverse transcription (RNase H +) to generate random sized cDNA. The nucleic acid can be RNA. Fragmented DNA can be treated with a base excision repair enzyme (eg, Endo VIII, formamide pyrimidine DNA glycosylase (FPG)) to excise damaged bases that may interfere with polymerization. The DNA can then be treated with a proofreading polymerase (eg, T4 DNA polymerase) to polish the ends and replace the damaged nucleotide (eg, an abasic site). In some embodiments, the DNA is not treated with a proofreading polymerase to polish the ends and replace damaged nucleotides.

  [00205] The nucleic acid (eg, DNA or RNA) can be treated with thermophilic phosphatase to remove phosphate groups from the nucleic acid. The reaction mixture can be heated to 80 ° C. for 10 minutes to inactivate the phosphatase and polymerase and denature double stranded DNA into single strands.

  [00206] The chemically or enzymatically phosphorylated adapter may be, for example, polyethylene glycol 10 to 10 having an average molecular weight of 4000, 6000, or 8000, regardless of whether or not it has a 3 'terminal affinity tag (eg, biotin). In the presence of 20% (w / v), a final concentration of 0.5 μM or more, containing a saturating amount of ATP-dependent RNA ligase (eg thermophilic such as T4 RNA ligase, Circ ligase, Circ ligase II etc.) About 12 to about 15 bases can be linked to the 3 'end of the fragmented single stranded nucleic acid. The reaction can be incubated at about 60 to about 70 ° C. for 1 hour. The adapter contains (i) all or part of the sequence corresponding to surface-bound oligonucleotides for Illumina flow cell clustering or not at all, and (ii) the interaction of the affinity ligand with the binding receptor At sufficient distance (eg, 10 atoms or more) to minimize steric hindrance, it includes a 3 'terminal affinity group that can not participate in the ligation reaction linked to the oligonucleotide.

  [00207] The adapter is adenylated by any means known in the art. When an adenylation adapter is used, in some embodiments, the ATP-dependent RNA ligase is not Circligase or Circligase II. In some cases, ATP-dependent RNA ligase is not required. The reaction can be purified by size to remove unreacted adapter. Purification can be achieved by use of a microfiltration unit with a molecular size cutoff of 10K or 3K (e.g. Microcon YM-10 or YM3, or Nanosep Omega). Adapter removal is selective precipitation with PEG, alcohols, or salts by using a spin column, by passing it through a size exclusion desalting column (agarose, polyacrylamide), for example using a size exclusion cut-off of 10 K or less By high stringency washing or by denaturing gel electrophoresis.

  [00208] The oligonucleotide primer that fully complements or partially complements the adapter at the 3 'end can include a sequence corresponding to the sequence on the flow cell, such as an Illumina flow cell oligonucleotide, and a proofreading mesophilic DNA polymerase Can be used to generate the reverse complement of the binding library. 5'-3 'exonucleolytic / interonucleolytic (eg Family A DNA polymerase, eg DNA polymerase I), or 3'-5' exonucleolytic (eg Family B DNA polymerase, Vent Thermophilic polymerases with (Phusion, Pfu, and variants thereof) activity can be used to enable linear amplification of the library.

  [00209] In some cases, the recovered material can then be attached to an affinity resin or support that can be attached to the 3 '-end affinity tag in batch mode. The recovered material can be placed on a pre-cleaned support in a 0.2 ml tube containing at least 10 times the total number of tagged adapter molecules, or 100 times more available binding sites.

[00210] The supernatant consisting of copies of the binding library can be harvested and quantified.
[00211] In one example, dsDNA is fragmented. The ds DNA fragment can be phosphorylated and heat denatured to make it single stranded. A biotin labeled adapter containing a primer docking sequence can be contacted with the nucleic acid fragment. Adapters can be ligated to the 3 'end of ssDNA fragments to generate precursors of library elements. A primer comprising a sequence complementary to the adapter and an additional adapter sequence (eg, the 5 'end of the primer) can be hybridized to ssDNA via the ligated adapter. The hybridized primers can be extended along the template ssDNA fragment to create a duplex. The duplex can be immobilized on a solid support (eg, streptavidin coated beads). Heat denaturation can release the final library element into solution while retaining the original ssDNA fragment on the beads.

[00212] G. Preparation of ssDNA library (attachment of adapters to both ends of fragments)
[00213] Provided herein are methods, compositions, and kits for preparing ssDNA libraries, comprising denatured dsDNA fragments into ssDNA, and ligating adapter sequences to both ends of ssDNA molecules. Methods for fragmenting dsDNA are described herein. Methods for denaturing ds DNA fragments are described herein.

  [00214] The method comprises a sequence that is at least 70%, 80%, 90%, or 100% complementary or identical to a first surface-bound oligonucleotide (eg, sequencing instrument flow cell oligonucleotide), Coupling of the first adapter can be included. The first surface binding oligonucleotide can be an NGS platform specific surface binding oligonucleotide. The first adapter is complementary or identical to at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20 or more consecutive nucleotides of the surface-bound oligonucleotide Can contain a sequence of The first adapter can further comprise a sequence that is at least 70%, 80%, 90%, or 100% complementary to the first sequencing primer. The first adapter can be ligated to the 3 'end of the ssDNA fragment using the methods described herein or any method known in the art. The ssDNA fragment may lack the 5 'phosphate group. The first adapter can be ligated to the 3 'end of the ssDNA fragment by an ATP-dependent ligase. The first adapter can comprise a 3 'end blocking group. The 3 'end blocking group can prevent the formation of a covalent bond between the 3' terminal base and another nucleotide. The 3 'end blocking group can be dideoxy-dNTP or biotin. The first adapter can be 5 'adenylated. The first adapter can be ligated to the 3 'end of the ssDNA fragment by RNA ligase as described herein. The RNA ligase can be an RNA ligase that has been cleaved or mutated from T4 or Mth. The method can further comprise linking a second adapter sequence to the 5 'end of the ssDNA fragment. The second adapter sequence can be separate from the first adapter sequence. The second adapter sequence can comprise a sequence that is at least 70% complementary to the second surface bound oligonucleotide. The second surface binding oligonucleotide can be an NGS platform specific surface binding oligonucleotide. The second adapter is complementary or identical to at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20 or more consecutive nucleotides of the surface-bound oligonucleotide Can contain a sequence of The second adapter can further comprise a sequence that is at least 70%, 80%, 90%, or 100% complementary to the second sequencing primer. The second adapter can be ligated to the ssDNA fragment using an RNA ligase, for example, a circ ligase as described herein. The first and second adapters are both at least 70%, 80%, 90%, or 100% complementary to the first and second surface bound oligonucleotides. The first and second adapters are both at least 70%, 80%, 90%, or 100% identical to the first and second surface bound oligonucleotides.

  [00215] The ssDNA libraries generated using the methods described herein can be used for whole genome sequencing or target sequencing. In some embodiments, a ssDNA library generated using the methods described herein is enriched for the target polynucleotide of interest prior to sequencing.

[00216] H. Formation of ssDNA library: enrichment of target specific library
[00217] Provided herein are methods, compositions, and kits for producing a target enriched nucleic acid library. The method can involve hybridizing a target selective oligonucleotide (TSO) to a single stranded DNA (ssDNA) fragment to create a hybridization product and extend to create an extended strand.

[00218] The method of target enrichment can be as described in US Patent Application Publication 20120157322, which is incorporated herein by reference.
[00219] Hybridization and amplification can occur in the reaction mixture. The term "reaction mixture" as used herein can refer to a mixture of components that amplify at least one amplicon from a nucleic acid template molecule. The mixture can include nucleotides (dNTPs), polymerases, and target selective oligonucleotides. The mixture can include multiple target selective oligonucleotides. The mixture can further include Tris buffer, monovalent salts, and Mg 2+. The concentration of each component can be further optimized by one skilled in the art. The reaction mixture may also contain nonspecific background / blocking nucleic acids (eg salmon sperm DNA), biopreservatives (eg sodium azide), PCR enhancers (eg betaine, trehalose etc), and inhibitors (eg RNAse) Additives can be included, including but not limited to inhibitors). A nucleic acid sample (eg, a sample containing ssDNA fragments) can be mixed with the reaction mixture. The reaction mixture can further comprise a nucleic acid sample.

  [00220] ssDNA fragments can be elements of ssDNA libraries. An ssDNA library can be generated using the methods described herein. The ssDNA fragment can comprise a first single stranded adapter sequence at a first end but not at a second end. The first end can be the 5 'end. The TSO can comprise a second single stranded adapter sequence at a first end but not at a second end. The first end can be the 5 'end. The first adapter sequence can comprise a sequence that is at least 70%, 80%, 90%, or 100% complementary or identical to the first surface-bound oligonucleotide (eg, flow cell oligonucleotide) . The first adapter sequence can comprise a sequence that is at least 70%, 80%, 90%, or 100% complementary or identical to the sequencing primer. The first adapter can include a barcode sequence. The second adapter can comprise a sequence that is at least 70%, 80%, 90%, or 100% identical to the second surface bound oligonucleotide (eg, flow cell oligonucleotide). The second adapter sequence can further include a sequence that is at least 70%, 80%, 90%, or 100% identical to the sequencing primer.

  [00221] Target selective oligonucleotides (TSOs) can be designed to at least partially hybridize to a target polynucleotide of interest. TSOs can be designed to selectively hybridize to target polynucleotides. The TSO can be at least about 70%, 75%, 80%, 85%, 90%, 95%, or more than 95% complementary to the sequence in the target polynucleotide. TSO can be 100% complementary to the sequence in the target polynucleotide. Hybridization can result in TSO / target duplexes containing Tm. TSO / target duplex Tm: 0 to about 100 ° C., about 20 to about 90 ° C., about 40 to about 80 ° C., about 50 to about 70 ° C., about 55 to about 65 ° C., or about 62 to about 68 It can be ° C. TSO can be of sufficient length to prime synthesis of extension products in the presence of a polymerase. The exact length and composition of TSO can vary depending on many factors, including the temperature of the annealing reaction, the source and composition of the primers, and the concentration ratio of primer to probe. The TSO can be, for example, about 8 to about 50 nt in length, about 10 to about 40 nt, about 12 to about 24 nt in length. The TSO can be about 40 nt in length. In some cases, the portion of TSO that binds the target sequence is about 10 to about 50 nt, about 20 to about 50 nt, about 25 to about 40 nt, about 30 to about 40 nt, or about 35 to about 40 nt.

  [00222] The TSO annealed to the target sequence can be elongated. Amplification can be performed using a nucleic acid polymerase. The nucleic acid polymerase can be a DNA polymerase. The DNA polymerase can be a thermostable DNA polymerase. The polymerase can be an element of A or B family DNA proofreading polymerases (Vent, Pfu, Phusion, and variants thereof), DNA polymerase holoenzyme (DNA por III holoenzyme), Taq polymerase, or a combination thereof .

  [00223] The extension can be performed as an automated process, circulating the reaction mixture containing the template DNA through the denaturation step, the primer annealing step, and the synthesis step. An automated process can be performed using a PCR thermal cycler. Commercially available thermal cycler systems include, among others, systems from Bio-Rad Laboratories, Life technologies, Perkin-Elmer.

  [00224] The TSO annealed to the target sequence is extended to produce an extension product comprising the second adapter sequence, the reverse complementary strand of the target sequence, and the reverse strand of the first adapter sequence. can do. If the first adapter sequence of the original ssDNA fragment is 70% or more identical to the first surface bound oligonucleotide, the extension strand is 70% or more complementary to the first surface bound oligonucleotide And can be hybridized to a first surface bound oligonucleotide (eg, flow cell oligonucleotide). The extension strand can comprise a target enriched library.

  [00225] The extension product annealed to the target sequence in the reaction mixture can be denatured. In some cases, the extension strand is subjected to amplification, eg, polymerase chain reaction, prior to use in massively parallel sequencing instruments or other applications. In some cases, the extension strand is not amplified (e.g., amplified in solution using PCR, etc.) prior to use in a massively parallel sequencing instrument or other application. In some cases, for example, about 5 to about 50 cycles, about 5 to about 40 cycles, about 5 to about 30 cycles, about 5 to about 50 cycles, for example, in solution, for the extension chain, prior to use in a massively parallel sequencing instrument. PCR is performed for about 25 cycles, about 5 to about 20 cycles, or about 5 to about 15 cycles. In some cases, for example, less than 40 cycles, less than 30 cycles, less than 25 cycles, less than 20 cycles, less than 15 cycles, less than 14 cycles, 13 cycles, for example, in solution prior to use in the massively parallel sequencing instrument Less than cycles, less than 12 cycles, less than 11 cycles, or less than 10 cycles, amplification is performed, eg, PCR. The extension strand may, for example, be approximately 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, in solution prior to use in a massively parallel sequencing instrument. It can be amplified by 19 or 20 cycles, eg PCR. The amplification is annealed to the first strand (e.g., a primer having the same sequence as the adapter sequence at the 5 'end of the target sequence) and the second strand of the second adapter sequence. A second primer (for example, a primer having the same sequence as the second adapter sequence at the 5 'end of TSO) can be used.

  [00226] contacting the denatured extension product, and / or its amplification with a surface immobilized on the product using at least a first surface bound oligonucleotide (eg, flow cell sequence) Can. The extension strand can be captured by a first surface bound oligonucleotide (eg, a flow cell oligonucleotide) that can anneal to a first adapter sequence on the extension strand.

  [00227] The first surface bound oligonucleotide can prime the elongation of the captured extension strand. Elongation of the captured extension strand can result in the captured extension product. The captured extension product is at least 70%, 80%, 90%, or 100% or more complementary to the first surface-bound oligonucleotide, to the target sequence, and to the second surface-bound oligonucleotide. And the complementary strand of the adapter sequence.

  [00228] The captured extension product can be hybridized to a second surface bound oligonucleotide to form a bridge. In some embodiments, the bridge is amplified by bridge PCR. The bridge PCR method can be performed using methods known in the art.

[00229] I. Kit for library preparation and target enrichment
[00230] Also provided is a kit for performing the preparation of the library as described herein or the method of target enrichment as described herein.

  [00231] The kit can include reagents for repair and chemical modification of dsDNA. The kit can include reagents for the purification of single stranded DNA. The kit can include one or more enzymes to excise damaged bases. The kit can include a phosphatase. The kit can include a kinase. The kit can include terminal transferase and dideoxynucleotides to block the 3 'end of the DNA fragment.

  [00232] Provided herein are kits for preparing ssDNA libraries. The kit comprises an adapter, for example as described herein. The kit can include instructions, eg, instructions for linking an adapter to the ssDNA fragment. The kit can further include a ligase. The ligase can be Rnl1 or Rnl2 family ligase. The kit can further include a primer capable of hybridizing to the adapter. Primers that are hybridizable to the adapter are described herein. The kit can provide a solid support, eg, beads immobilized on a capture reagent. The kit can provide a polymerase that causes the extension reaction. The kit can provide dNTPs that cause the extension reaction.

  [00233] The kit comprises a first adapter comprising a sequence that is at least 70%, 80%, 90%, or 100% complementary or identical to a first support bound oligonucleotide coupled to a sequencing platform An oligonucleotide, a second adapter oligonucleotide comprising a sequence distinct from the first adapter, an RNA ligase, and instructions for use can be included. The first adapter can include a 3 'end blocking group that prevents the formation of a covalent bond between the 3' terminal base and another nucleotide. The 3 'end blocking group is described herein. The first adapter can be 5 'adenylated. The first adapter can comprise a sequence that is at least 70%, 80%, 90%, or 100% complementary or identical to the sequencing primer. The second adapter can comprise a sequence that is at least 70%, 80%, 90%, or 100% complementary or identical to the sequencing primer. The second adapter can comprise a sequence that is at least 70%, 80%, 90%, or 100% complementary to a second surface-bound oligonucleotide coupled to a sequencing platform.

  [00234] Also provided is a kit for preparing a target enriched DNA library. The kit can comprise an adapter, a ligase, a primer capable of hybridizing to a target specific sequence, a solid support comprising a capture reagent, a polymerase, dNTPs, or any combination thereof. TSO is free in solution, or as solid support coupled for sequencing on an NGS platform, as described in US Patent Application Publication 20120157322, which is incorporated herein by reference. It can be immobilized on the body.

  [00235] The kits provided herein can include packaging material. The term "packaging material" can refer to a physical structure that contains the components of the kit. The packaging material can maintain the sterility of the components of the kit and can be made of materials commonly used for such purposes (eg, paper, corrugated fibers, glass, plastics, foils, ampoules, etc.). The kit can also include buffers, preservatives, or protein / nucleic acid stabilizers.

[00236] The disclosure provided herein can include employing molecular biology techniques, microbiology techniques, and recombinant DNA techniques within the skill of the art. For example, Sambrook, Fritsch & Maniatis, Molecular Cloning: A Laboratory Manual, Fourth Edition (2012); Oligonucleotide Synthesis (M. J. Gait, ed., 1984); Nucleic Acid Hybridization (B. D. Hames & S. J. Higgins, eds., 1984); A Practical Guide to Molecular Cloning (B. Perbal, 1984); and Methods in Enzymology series (Academic Press, Inc.). All patents, patent applications, and publications mentioned herein, both supra and infra, are hereby incorporated by reference.
IX. Patient monitoring
[00237] The computing systems, software media, methods, and kits provided herein can be used for patient monitoring, eg, longitudinal assays. Methods are from primary tumor samples such as formalin fixed paraffin embedded (FFPE) samples, fine needle aspiration (FNA) biopsies, core needle biopsies (CNB), and / or cell free samples (eg, cell free plasma samples) , Sequencing of one or more genes, such as massively parallel sequencing (next generation sequencing). The initial sample can be a sample obtained from the subject prior to the subject undergoing cancer treatment. When plasma is used as the initial sample, the amount of DNA used from the sample can be about 1 ng of DNA. When plasma is used as an initial sample, the volume of plasma can be about 3 mL. In some cases, only solid tumor samples (eg, FFPE samples, FNA samples, or CNB samples) are obtained from the subject prior to the subject receiving cancer treatment for sequencing, and nucleic acids from the sample are sequenced. Ru. In some cases, only the fluid sample (eg, plasma) is obtained from the subject for sequencing prior to the subject receiving cancer treatment, and the nucleic acid is sequenced from the fluid (eg, plasma) sample. In some cases, both a solid tumor sample and a fluid sample (eg, plasma) are obtained from the subject for sequencing prior to the subject being treated for cancer, the nucleic acid is a solid tumor sample and fluid (eg, plasma) ) Sequenced from a sample. Sequencing data from solid tumor and fluid samples obtained before the subject receives cancer treatment can be compared. In some cases, sequencing data from solid tumor and fluid samples obtained before the subject receives cancer treatment is not compared.

  [00238] The number of genes to be sequenced in a sample (eg, an initial sample) is approximately or at least 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 96, 100, 110, 120, 129, 130, 140, 150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700, 800, 900, or more genes. Sequencing can be performed in a modified clinical laboratory improvement (CLIA) approved laboratory and / or an American Pathologists Association (CAP) approved laboratory. Analysis of sequencing data (eg, bioinformatics) can be performed in CLIA and / or CAP approved laboratories. The sequenced gene can be one or more of the following: ABCA1, BRAF, CHD5, EP300, FLT1, ITPA, MYC, PIK3R1, SKP2, TP53, ABCA7, BRCA1, CHEK1, EPHA3, FLT3, JAK1, MYCL1, PIK3R2, SLC19A1, TP73, ABCB1, BRCA2, BRCA2, BRCA2, BRCA2 JAK2, MYCN, PKHD1, SLC1A6, TPM3, ABCC2, BRIP1, CLTC, EPHA6, FN1, JAK3, MYH2, PLCB1, SLC22A2, TPMT, ABCC3, BUB1B, COL1A1, EPHA7, FOS, JUN, MYH9, PLCG1SL0, SL3 ABCC4, Chlorf144, COPS5, EPHA8, FOXO1, KBTBD11, NAV3, PLCG2 SMAD2, TPR, ABCG2, CABLES1, CREB1, EPHB1, FOXO3, KDM6A, NBN, PML, SMAD3, TR10, ABL1, CACNA2D1, CREBBP, EPHB4, FOXP4, KDR, NCOA2, PMS2, SMAD4, TRRAP, ABL2CRK, KBL EPHB6, GAB1, KIT, NEK11, PPARG, SMARCA4, TSC1, ACVR1B, CARD11, CRLF2, EPO, GATA1, KLF6, NF1, PPARGC1A, SMARCB1, TSC2, ACVR2A, CARM1, CSF1R, ERBB2, GLI1, KLHDC4, NF2 SMO, TTK, ADCY9, CAV1, CSMD3, ERBB3, GLI3, KRA , NKX2-1, PPP2R1A, SOCS1, TYK2, AGAP2, CBFA2T3, CSNK1G2, ERNK4, GNA11, LMO2, NOS2, PPP2R1B, SOD2, TYMS, AKT1, CBL, CTNNA1, ERCC1, GNAQ, LRP1 BNOS, , AKT2, CCND1, CTNNA2, ERCC2, GNAS, LRP2, NOTCH1, PRKCA, SOX10, UMPS, AKT3, CCND2, CTNNB1, ERCC3, GPR124, LRP6, NOTCH2, PRKCZ, SOX2, USP9X, ALK, CCND3, CYFIP1 , LTK, NOTCH3, PRKDC, SP1, VEGF, ANAPC5, CCNE1, YLD, ERCC5, GRB2, MAB1B1, NPM1, PTCH1, SPRY2, VEGFA, APC, CD40LG, CYP19A1, ERCC6, GSK3B, MAP2K1, NQO1, PTCH2, SRC, VHL, APC2, CD44, CYP1B1, ERG, GSTP1, MAP2K2, NR3Cl PTEN, ST6GAL2, WRN, AR, CD79A, CYP2C19, ERN2, GUCY1A2, MAP2K4, NRAS, PTGS2, STAT1, WT1, ARAF, CD79B, CYP2C8, ESR1, HDAC1, MAP2K7, NRP2, NRP2, STAT3, XPA, ARFRP1, CDC42, CYP2D6, ESR2, HDAC2, MAP3K1, NTRK1, PTPRB, STK11, XP C, ARID1A, CDC42BPB, CYP3A4, ETV4, HGF, MAPK1, NTRK2, PTPRD, SUFU, ZFY, ATM, CDC73, CYP3A5, EWSR1, HIF1A, MAPK3, NTRK3, RAD50, SULT1A1, ZNF521, ATP5A1, CDH1, DACH1, DACH1 HM13, MAPK8, OMA1, RAD51, ATR, CDH10, DCC, EZH2, HMGA1, MARK3, OR10R2, RAFT, TAF1, AURKA, CDH2, DCLK3, FANCA, HNF1A, MCL1, PAK3, RARA, TBX22, AURKB, CDH2O DDB2, FANCD2, HOXA3, MDM2, PARP1, RB1, TCF12, BAI3, CDH5, DB2, FANCE, HOXA9, MDM4, PAX5, REM1, TCF3, BAP1, CDK2, DGKB, FANCF, HRAS, MECOM, PCDH15, RET, TCF4, BARD1, CDK4, DGKZ, FAS, HSP90AA1, MEN1, PCDH18, RICTOR, TEK, BAX, CDK6, DIRAS3, FBXW7, IDH1, MET, PCNA, RIPK1, TEP1, BCL11A, CDK7, DLG3, FCGR3A, IDH2, MITF, PDGFA, ROR1, TERT, BCL2, CDK8, DLL1, FES, IFNG, MLH1, PDGFB ROR2, TET2, BCL2A1, CDKN1A, DNMT1, FGFR1, IGF1R, MLL, PDGFRA, ROS1, TGFBR2 BCL2L1, CDKN1B, DNMT3A, FGF2R, MLL3, PDGFRB, RPS6KA2, THBS1, BCL2L2, CDKN2A, DNMT3B, FGFR3, IPL, PDZRN3, RPTOR, TNFAIP3, BCL3HJT7G7 RSPO2, TNKS, BCL6, CDYD2, FH, IL2RG, MSH2, PIK3C3, RSPO3, TNKS2, BCR, CDKN2D, E2F1, FHOD3, INHBA, MIK6, PIK3CA, RUNX1, TNNI3K, BIRC5, CDX, EDS MTHFR, PIK3CB, SDHB, TNR, BIRC6, CEBPA , EGF, FLG2, IRS1, MTOR, PIK3CD, SF3B1, TOP1, BLM, CERK, EGFR, FLNC, IRS2, MUTYH, PIK3CG, SHC1, and TOP2A.

  [00239] Sequence data can be used to determine the profile of mutations in a gene. Mutation profiles can be listed in the report. The report can be provided to a caregiver or to a subject who has taken one or more samples. The report can indicate possible treatment options based on the mutation profile.

  [00240] Subsequent samples can be obtained from the subject after obtaining the initial sample, eg, to monitor one or more genes sequenced in the initial sample. Multiple subsequent samples can be obtained from the subject (e.g., approximately or at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90 , 100 samples). The subsequent sample from the subject can be a fluid sample, such as a plasma sample, or a sample from a solid tumor. Nucleic acids, such as cell free nucleic acids, such as cell free DNA from subsequent samples can be analyzed. Nucleic acids from subsequent samples can be analyzed by sequencing, eg, massively parallel sequencing (next-generation sequencing). The nucleic acid of the subsequent sample can be analyzed, for example, by amplification of PCR, eg digital PCR (dPCR), eg droplet digital PCR (eg ddPCR). The nucleic acid of subsequent samples can be analyzed by both amplification (eg dPCR, eg ddPCR) and sequencing, eg massively parallel sequencing (next generation sequencing).

  [00241] Subsequent samples can be obtained from the subject at regular or irregular intervals. Subsequent samples can be obtained from the subject daily, once a week, twice a month, once a month, once every three months, once a half year, or once a year.

  [00242] In some cases, subsequent samples can be analyzed by sequencing until sequencing no longer provides sufficient sensitivity to detect mutations or alterations in the identified gene in the initial sample. For example, mutations can be identified in genes by sequencing of nucleic acids from early solid tumor samples or early cell free samples (eg, plasma) (eg, using Illumina® MiSeq), Sequencing can be used to detect whether a mutation is present in the gene of the subsequent sample (eg fluid sample, eg plasma), sequencing further the mutation in the gene of the subsequent sample If it becomes undetectable, the amplification based assay (eg, dPCR, eg, ddPCR using Bio-Rad instrument QX 200TM Droplet DigitalTM PCR system, etc.,) suddenly in the gene of subsequent samples Whether or not a mutation is present can be detected. In some cases, amplification based methods such as dPCR, eg ddPCR, may have higher sensitivity than sequencing based methods. In some cases, mutations detected in the initial sample will not be detected in subsequent samples analyzed by sequencing, but will be detected in subsequent samples analyzed by amplification, eg ddPCR. In some cases, mutations present in the initial sample will not be detected in subsequent samples analyzed by sequencing and will not be detected in subsequent samples analyzed by amplification (eg ddPCR).

  [00243] The number of genes analyzed in the subsequent sample is less than the number of genes analyzed in the initial sample, the same number as analyzed in the initial sample, or analyzed in the initial sample It may be more than the number of genes. Genes analyzed in subsequent samples may be a subset of genes analyzed in initial samples. The genes analyzed in subsequent samples may be based on the profile of mutations identified in the initial sample (profiles of the individual variants). The number of genes analyzed in subsequent samples is approximately or at least 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 96, 100, 110, 120, 129, 130, 140 , 150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700, 800, 900, or more genes. In some cases, the number of genes analyzed in subsequent samples may be greater than the number of genes analyzed in initial samples. Analyze genes monitored in subsequent samples to monitor cancer, monitor treatment efficacy, detect cancer evolution, detect cancer recurrence, detect cancer relapse Or can detect the progression of the cancer.

  [00244] Subsequent samples can be analyzed over the period of cancer in the subject. If a cancer recurrence is identified in a subsequent sample, a second sample can be obtained from the subject and sequencing can be performed. The second sample can be a solid sample or a fluid sample (eg, a cell free sample), obtained from the subject, sequencing, eg, massively parallel sequencing (next generation sequencing), and The profile can be determined. In some cases, the second sample is a solid tumor sample and sequencing of nucleic acids from the solid tumor sample is performed.

  [00245] Sequencing is gene amplification, for example, at least 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 98.5% of the gene amplifications being tested. , 99%, 99.5%, or 100% can be detected. Gene amplification in a sample can be detected by digital PCR, eg ddPCR. The use of ddPCR is at least 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 98.5%, 99%, 99.5% of the gene amplification being tested. % Or 100% can be detected. Gene amplification can be detected using, for example, fluorescence in situ hybridization (FISH).

[00246] In some embodiments, a target enrichment library generated as described herein, using any method known in the art or as described herein , Sequencing is performed. Sequencing can reveal the presence of mutations in one or more cancer-related genes in the set. In some embodiments, a subset of the two, three or four genes with mutations hidden is assessed for cell-free DNA in a fluid sample that is isolated from the subject at a later time point for further monitoring It is selected. In some embodiments, a subset of no more than four genes for which a mutation is to be screened is selected for further monitoring by assessment of cell free DNA in fluid samples isolated from the subject at a later time point.
X. Definition
[00247] As used herein and in the appended claims, the singular forms "a", "an" and "the" can include plural unless the context clearly dictates otherwise. For example, the term "a cell" can include a plurality of cells, including mixtures of cells.

  [00248] Ranges may be expressed herein as from one particular value that is "about", and / or to another particular value that is "about". When such a range is expressed, another embodiment includes from the one particular value and / or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent "about," it will be understood that the particular value forms another embodiment. Furthermore, it will be appreciated that the endpoints of each of the ranges are significant in relation to the other endpoint, and independently of the other endpoint. The term "about" as used herein, within the context of a particular usage, refers to the range ± 15% from the stated numerical value. For example, about 10 would include the range of 8.5-11.5.

  [00249] The nucleic acids used in the processes described herein can be free in solution. The term "free in solution" can describe a solid support, such as a bead or a molecule such as a polynucleotide that is not bound or anchored to a flow cell.

  [00250] The processes described herein can utilize fragments of genomic DNA or genomic fragments. The term "genomic fragment" can refer to a region of the genome of an animal or plant, such as the genome of a human, monkey, rat, fish, or insect, or plant, for example. The genomic fragments may or may not be adapter linked. Genomic fragments can be adapter-linked (in which case with one or both ends of the fragment, an adapter linked to at least the 5 'end of the molecule) or not.

[00251] In certain instances, the oligonucleotides used in the methods described herein sequence a reference genomic region, ie, a genomic region of a known nucleotide sequence, eg, the NCBI Genbank database or other database It can be designed using the chromosomal region in which it is deposited.
Example

Identification of somatic cell variants
[00252] The subject undergoes colonoscopy and is found to harbor a colon tumor. Both tumor biopsy and blood collection are collected from the subject and used to support the subject's diagnosis of colon cancer. Sequencing is performed on tumor cells and normal cells from the first blood draw. Sequence comparisons of the subject's tumor with normal samples are based on probabilistic models and statistical inferences. The comparison takes advantage of known chromosomal loci in tumor gene mutations that have been reported in public databases and stochastically models possible sequences near the locus. Statistical inference is performed by combining the model with the subject's sequence data. Inference identifies point mutations of three somatic variants in the APC, KRAS, and TP53 genes. The stage of the subject's cancer is determined.

  [00253] Further, the data analysis application recommends a first treatment strategy, such as surgery to remove the tumor. In the first treatment, a second blood draw is performed. It is determined that the subject's tumor has metastasized. The subject is dosed as a second therapy (chemotherapy) to manage cancer.

Data analysis by Bayesian network
[00254] FIG. 8 shows an exemplary Bayesian network that illustrates inference for target use cases. In the network diagram, node "C" represents the inferred variant call, node "R" represents the base call of a series of aligned reads across loci, and node "P" is a multiple relationship at a locus (For example, it is diploid in the case of normal germline but may differ in cancer cells due to genomic instability). In the case of a sample containing cancerous tumor cells or DNA, "U" represents the cellularity of the sample, which can be estimated by other means (e.g. pathology) and DNA molecules from germline are It is indicated as the probability present in the tumor sample and is provided as a value of 0-1.

[00255] The following conditional probability distributions (CPDs) can be provided with appropriate values. (A) P (R | C), the probability of a series of reads given a specific variant call, (b) P (C _t | C _g ), the primary tumor given the germline at that locus Probability of cells, and (c) P (C _cf | C _t ), probability of tumor call in cell-free DNA (cf-DNA) given call in primary tumor sample.

[00256] CDP P (R | C) can be part of a standard Bayesian mutant calling methodology for single samples. The second two CPDs can be calculated by utilizing empirical values of somatic mutation rates that can be adjusted for each tumor type and signature of the dominant mutation. In the case of P (C _t | C _g ), and by assuming a simple phylogenetic relationship between the primary tumor and the tumor DNA detected in the acellular fraction of the patient's plasma, this CPD is It is possible to calculate, for example, with analogues, using calculations carried out in lineage, including deduction of de novo mutations in offspring, assuming simple inheritance of the variants without.

[00257] In addition, site- and allele-specific priors based on prior observation of germline variants by population sequencing, or based on a large-scale census of somatic mutations across tumor types such as the TCGA project. Can be introduced at a specific locus. These may be useful in the absence of a portion of a tissue sample (eg, germline or primary tissue) from a patient. In one case, only the primary tumor tissue or only the cf-DNA from the plasma fraction is analyzed. In this situation, using prior information, P (C _t | C _tp ) (C _tp is based on prior observation of cancer patients (eg by COSMIC), somatic mutations specific to that locus To estimate CPD, such as prior probability of observing alleles), and P (G _t | G _p ) (G _t is the genotype of a germline variant present in a tumor given G _p ) The probability of observing a particular genotype at this locus is derived from a population-wide mutation survey (such as the 1000-man genome project). These probabilities are then provided as a score for each variant analyzed in the output, recalibrated based on empirical verification or ground truth data using machine learning methods, and then used by the analyst Thus, appropriate FP / FN thresholds for downstream annotation and clinical reporting can be determined.

[00258] Another factor to consider is the cellularity of the cancer sample, ie, the cancer contained in a biological specimen (eg, biopsy, plasma, etc.) against normal cells (representing germline DNA) It is the proportion of tissue (and hence DNA). When cellularity is low, the probability that the variant is germline is increased and vice versa. To explain this factor, introduce a random variable "U" into the Bayesian network, which is the reciprocal of cellularity, ie the probability that the sequencing read is from germline cells (value 0-1) be able to. This value can be provided at analysis time, but in some cases it can be inferred from the data by providing a prior estimate. When discussing cellularity, two new CPDs can be deduced: P (A _t | R _t ) and P (A _ct | R _ct ). These can be incorporated into the inference of calls by standard Bayesian techniques.

  [00259] Finally, the population calling method can also be combined with the method, detecting germline mutations in normal tissue by calling simultaneously with a bank of data from other samples according to the method described above Can be improved (and at the same time reduce false positive somatic mutations), but is applied in the context described herein, which calls the germline simultaneously with the cancer tissue sample.

Lung cancer analysis
[00260] Examine lung cancer patients. A biopsy is performed to extract tumor tissue and normal tissue. In addition, the patient's blood is collected. The samples (i.e. tumor tissue, normal tissue and blood) are sequenced by a high throughput sequencer. The sequencer generates a large number of sequence reads. The systems disclosed herein compare sequences across samples to align sequences. In addition, the reference human genome is used in the alignment process.

  [00261] After completing the alignment, the genomes of tumor tissue, normal tissue, and blood are generated. A sliding window is applied to three genomes simultaneously. The sliding window covers the same chromosomal locus. Assessing the sequences in the window across the sample allows the data analysis application to identify putative variants. The uncertainty of the variants is captured by a probabilistic model. Based on published or known databases or existing information published in patients analyzed in the past, the likelihood that the somatic variant characterizes the stage of the cancer is calculated. In addition, the likelihood of additional variants representing markers of optimal treatment strategy is calculated as well. These calculated likelihoods allow the physician to better understand the patient's current condition and to design the best health care for the patient.

Somatic point mutation / small indel caller
[00262] Targeted resequencing of tumor samples is performed on a region of nucleic acid encompassing approximately 100 kB, including approximately 129 available oncogenic exons. In some cases, the resequencing region also includes an intron region to detect translocations. The average depth of sequencing is about 300x to about 500x, including variability within the application range. Only a few rounds of PCR amplification are performed on the DNA library. The paired end read length is 250 bp for MiSeq and 150 bp for HiSeq. Overlap of paired end leads is possible for long leads of MiSeq. Both strands of the region can be captured independently, then mixed and sequenced. The fragments can have a median size of about 200 to about 300 bp. Off-target reads outside the region of interest are leveraged for sample identification, large scale deletion / aneuploidy / fusion detection, and genomic scar analysis (genomic scarring may be loss of genome due to known origin) .

  [00263] The methods, systems, and computer readable media provided herein can be used only when tumor data, eg, pathology specimens treated as FFPE blocks, are available. The methods, systems, and computer readable media provided herein can be used only when plasma derived from cell free DNA is sequenced. The methods, systems, and computer readable media provided herein, for example, when sequencing cell-free DNA from plasma and sequencing germline sequences, for example, the buffy coat is isolated from blood, reproduction It can be used when sequencing to represent cell lineage tissue (lymphocytes). The methods, systems, and computer readable media provided herein can be used where tumor and germline samples are available in addition to cell free DNA. Germline sequences can be from buffy coat or other tissue biopsies.

  [00264] The method may involve the input of FastQ formatted sequence information. The leads can be aligned to sensitive genomic assemblies. Alignments are stored as CRAM files or BAM files. The output is VCF (mutant cell format). Small single base substitution mutations (SNV), polybasic polymorphism (MNP), and small indels in the target region are designated as BED files. Allelic calls are made without the assumption of multiple relationships (eg, low frequency of occurrence in allele counts). In the case of putative somatic mutations, variant allele frequency of occurrence (VAF) is indicated by VCF. No diploid genotype is provided. In the case of putative germline mutations, potential diploid genotypes are provided. Prior knowledge of common appropriate variants in the population (static VCF with MAF (mutation annotation format)) helps to differentiate germline mutations with somatic mutations. Simultaneous calling of patient samples can be performed if available. If no germline samples from the patient are available, simultaneous calling with the bank of "normal" germline samples sequenced by the target sequencing method described herein (best sample size determined ). Prior knowledge of recurrent somatic mutations in cancer (eg, using COSMIC) can be considered as an aid in differentiating somatic mutations. Calls are made at all locations across the area of interest, creating innovative reference calls and no calls (as needed). A compressed reference call on the gVCF output can be implemented to limit the size of the VCF. Variant scores for likelihood, which are somatic and germline variants, can be provided. Customized score recalibration is performed based on training data. The cellularity index can be examined when available for tumor and cell-free DNA samples (data-based reasoning). Variant calls are provided to all off target regions. In order to improve the call accuracy, it can be considered whether pair-end lead overlap is available (250 bp read of MiSeq).

  [00265] Molecular barcodes can be detected to identify duplicate fragments and provide error correction. Also, replicate reads can be used as independent sequencing events to recondition the score based on redundant sequencing.

  [00266] While preferred embodiments have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, variations and substitutions will occur to those skilled in the art without departing from the invention. It should be understood that alternatives to the embodiments described herein may be used in the practice of the present disclosure. It is intended that the following claims define the scope of the present invention and encompass methods and structures within the scope of these claims and their equivalents.

Claims (123)

(A) a processor and a memory module configured to execute machine readable instructions;
(B) a data analysis application,
(1) Data configured to receive sequence reads of nucleic acid molecules obtained from one or more samples of an individual, generated by a high-throughput sequencing instrument. A receiving module,
(2) a sequence alignment module configured to align the sequence reads to a reference assembly to generate a predicted genomic sequence;
(3) (i) identify a putative variant by jointly analyzing said predicted genomic sequence simultaneously; (ii) somatic mutation or germline variant A data analysis application comprising: a genome analysis module configured to score the putative variants by probability.   The system of claim 1, wherein the one or more samples are collected simultaneously.   3. The system of claim 1 or 2, wherein the one or more samples comprise at least two samples, and the at least two samples are collected at different times.   The system according to any one of claims 1 to 3, wherein the one or more samples comprise a primary tumor.   5. The system of any one of claims 1 to 4, wherein the one or more samples comprise metastatic tumors.   6. The system of any one of the preceding claims, wherein the one or more samples comprise bodily fluid.   7. The system of any one of claims 1 to 6, wherein the one or more samples comprise a cell free sample.   7. The system of any one of claims 1 to 6, wherein the one or more samples comprise lymphocytes.   9. The system of any one of the preceding claims, wherein the one or more samples comprise plasma.   10. The method according to any one of claims 1 to 9, wherein said identification of said putative variant comprises comparing said genomic sequence to a sequence of a bank of sequences obtained from one or more previously analyzed patients. system.   11. The method according to any one of the preceding claims, wherein the scoring of the putative variants comprises adjusting the probability based on machine learning methods trained with the correct and false call pairs. system.   12. The system according to any one of the preceding claims, wherein the identification and scoring of the putative variants comprises making inferences at chromosomal loci.   13. The system of claim 12, wherein the creating of inference comprises using a probabilistic model.   14. A system according to claim 12 or 13, wherein the creation of inference comprises using statistical inference.   The system according to any one of claims 12 to 14, wherein the creation of inference comprises using Bayesian inference.   The system according to any one of claims 12 to 15, wherein said creating of inference comprises using a Bayesian network model.   17. The system according to any one of claims 12 to 16, wherein the generation of inference is based on prior probabilities of finding germline and somatic variants.   18. The system according to any one of claims 12-17, wherein the generation of inference is based on a series of sequence reads aligned across the chromosomal loci.   19. The system of any one of claims 12-18, wherein the creation of inference is based on an error rate of the high throughput sequencing device.   20. The system of claim 19, wherein the error rate is provided in quality assurance for a base call.   21. The system according to any one of claims 12 to 20, wherein the creation of inference is based on a multiple relationship of chromosomal regions across the chromosomal loci.   22. The system according to any one of claims 12 to 21, wherein said creation of inference is based on a process model of clonal evolution of cancer.   23. The system according to any one of claims 12-22, wherein the creation of inferences is based on calls at chromosomal loci derived from one or more other samples of the individual.   24. The system according to any one of claims 12 to 23, wherein said creating of inferences is based on calls at chromosomal loci derived from one or more samples of one or more other individuals.   25. The system according to any one of claims 12 to 24, wherein said creating of inferences is based on prior knowledge of common polymorphisms at chromosomal loci of one or more reference populations.   26. The system according to any one of claims 12-25, wherein said creation of inference is based on prior knowledge of mutations of one or more recurrent cancers at chromosomal loci.   27. The system according to any one of claims 12 to 26, wherein the production of inference is based on the proportion of cancer cells in a sample containing cancer.   28. The system of claim 27, wherein the sample containing cancer comprises one or more DNA molecules that cause cancer.   29. The system of claim 27 or 28, wherein the sample containing cancer comprises one or more cancerous tissues.   30. The system according to any one of claims 12-29, wherein said creating of inference comprises describing a variant by means of a probabilistic model.   31. The system according to any one of claims 12 to 30, wherein said creating of inference comprises describing a series of aligned sequence reads across said chromosomal loci by means of a probabilistic model.   32. The system according to any one of claims 12 to 31, wherein the creation of inference comprises describing a multiple relation at the chromosomal locus by means of a probabilistic model.   33. The system according to any one of claims 12 to 32, wherein said creating of inference comprises describing the proportion of cancer cells in a sample by means of a probabilistic model.   34. The system of claim 33, wherein the ratio is described by a binary variable.   The data analysis application includes: one or more coding regions, expected severity of damage, one or more germline mutations, one or more somatic mutations, one or more mutations and drugs The putative variants are annotated as to their effect on one or more of the interaction, one or more mutations observed in a clinical trial, one or more diseases, one or more symptoms, or one or more side effects. 35. The system of any one of the preceding claims, further comprising a module configured to attach.   36. The system according to any one of the preceding claims, wherein the data analysis application further comprises a module configured to recommend a treatment method.   37. The system according to any one of the preceding claims, wherein the data analysis application further comprises a module configured to recommend a treatment method.   The system according to any one of the preceding claims, wherein the data analysis application further comprises a module configured to assess the progress of treatment.   39. The system of any one of claims 1-38, wherein the data analysis application further comprises a module configured to assess risk.   40. The system according to any one of the preceding claims, wherein the data analysis application further comprises a module configured to monitor the efficiency of the treatment method.   41. The system according to any one of the preceding claims, wherein the data analysis application further comprises a module configured to monitor the efficiency of the treatment method. A computer readable storage medium encoded with a computer program comprising instructions executable by a processor to create a data analysis application, the application comprising
(A) a data receiving module configured to receive a sequencing read of nucleic acid molecules obtained from one or more samples of an individual generated by a high throughput sequencing device;
(B) a sequence alignment module configured to align the sequence reads with respect to a reference assembly to generate a predicted genomic sequence.
(C) (i) identify putative variants by jointly analyzing the predicted genomic sequences simultaneously and (ii) score the putative variants by their probability of being a somatic mutation or germline variant And a data analysis application, comprising: a genome analysis module configured as follows.   43. The medium of claim 42, wherein the one or more samples are collected simultaneously.   44. The medium of claim 42 or 43, wherein at least one or more samples are collected at different times.   45. The medium of any one of claims 42-44, wherein the one or more samples comprise a primary tumor.   46. The medium of any one of claims 42-45, wherein the one or more samples comprise metastatic tumors.   47. The medium of any one of claims 42-46, wherein the one or more samples comprise bodily fluid.   48. The medium of any one of claims 42-47, wherein the one or more samples comprise a cell free sample.   48. The medium of any one of claims 42-47, wherein the one or more samples comprise lymphocytes.   50. The medium of any one of claims 42-49, wherein the one or more samples comprise plasma.   51. The method according to any one of claims 42 to 50, wherein the identification of the putative variant comprises comparing the genomic sequence to a sequence from a bank of sequences obtained from one or more previously analyzed patients. Media.   52. The scoring according to any one of claims 42 to 51, wherein said scoring of said putative variants comprises adjusting probabilities based on machine learning methods trained with correct and false call pairs. Medium.   53. The medium of any one of claims 42-52, wherein the identifying and scoring of the putative variant comprises making an inference at a chromosomal locus.   54. The medium of claim 53, wherein the creating of inference comprises using a probabilistic model.   55. The medium of claim 53 or 54, wherein said creating of inferences comprises using statistical inference.   56. The medium of any one of claims 53-55, wherein the creating of inference comprises using Bayesian inference.   57. A medium according to any one of claims 53 to 56, wherein said creating of inference comprises using a Bayesian network model.   58. A medium according to any one of claims 53 to 57, wherein the generation of inference is based on prior probabilities of finding germline and somatic variants.   59. The medium of any one of claims 53 to 58, wherein the generation of inference is based on a series of sequence reads aligned across the chromosomal loci.   60. The medium of any one of claims 53-59, wherein the creation of inference is based on an error rate of the high throughput sequencing device.   61. The medium of claim 60, wherein the error rate is provided in quality assurance for a base call.   62. A medium according to any one of claims 53 to 61, wherein the creation of inference is based on a multiple relationship of chromosomal regions across the chromosomal loci.   63. A medium according to any one of claims 53 to 62, wherein the creation of inference is based on a process model of clonal evolution of cancer.   64. The medium according to any one of claims 53 to 63, wherein the creation of inferences is based on calls at chromosomal loci derived from one or more other samples from the individual.   65. The medium according to any one of claims 53 to 64, wherein the creation of inferences is based on calls at chromosomal loci derived from one or more samples of one or more other individuals.   66. A medium according to any one of claims 53 to 65, wherein the generation of inference is based on prior knowledge of common polymorphisms at chromosomal loci of one or more reference populations.   67. The medium according to any one of claims 53 to 66, wherein said generating of inferences is based on prior knowledge of one or more recurrent cancer mutations at chromosomal loci.   68. The medium of any one of claims 53 to 67, wherein said generating of inferences is based on the proportion of cancer cells in a sample containing cancer.   69. The medium of claim 68, wherein the sample containing cancer comprises one or more DNA molecules that cause cancer.   69. The medium of claim 68, wherein the sample containing cancer comprises one or more cancerous tissues.   71. The medium according to any one of claims 53 to 70, wherein said creating of inference comprises describing a variant by means of a probabilistic model.   72. A medium according to any one of claims 53 to 71, wherein the creation of inference comprises describing a series of aligned sequence reads across the chromosomal locus by a probabilistic model.   73. A medium according to any one of claims 53 to 72, wherein the creation of inference comprises describing a multiple relation at the chromosomal locus by means of a probabilistic model.   74. The medium of any one of claims 53-73, wherein said creating of inference comprises describing the proportion of cancer cells in a sample by means of a probabilistic model.   75. The medium of claim 74, wherein the ratio is described by a binary variable.   The data analysis application includes: one or more coding regions, expected severity of damage, one or more germline mutations, one or more somatic mutations, one or more mutations and drugs The putative variants are annotated as to their effect on one or more of the interaction, one or more mutations observed in a clinical trial, one or more diseases, one or more symptoms, or one or more side effects. 76. The medium of any one of claims 42-75, further comprising a module configured to attach.   78. The medium of any one of claims 42-76, wherein the data analysis application further comprises a module configured to recommend a treatment method.   78. The medium of any one of claims 42-77, wherein the data analysis application further comprises a module configured to recommend a treatment method.   79. The medium of any one of claims 42-78, wherein the data analysis application further comprises a module configured to assess the progress of treatment.   80. The medium of any one of claims 42-79, wherein the data analysis application further comprises a module configured to assess risk.   81. The medium of any one of claims 42-80, wherein the data analysis application further comprises a module configured to monitor the efficiency of a treatment method.   82. The medium of any one of claims 42-81, wherein the data analysis application further comprises a module configured to monitor the efficiency of a treatment method. (A) collecting one or more samples from an individual;
(B) sequencing the nucleic acid molecules of the one or more samples using high throughput sequencing equipment to generate a sequence read;
(C) aligning the sequence reads to a reference assembly to generate a predicted genomic sequence;
(D) identifying putative variants by jointly analyzing the predicted genomic sequences simultaneously;
(E) scoring the putative variants by their probability of being somatic mutations or germline variants.   84. The method of claim 83, wherein the one or more samples are collected simultaneously.   85. The method of claim 83 or 84, wherein the one or more samples comprise at least two samples, and the at least two samples are collected at different times.   86. The method of any one of claims 83-85, wherein the one or more samples comprise a primary tumor.   87. The method of any one of claims 83 to 86, wherein the one or more samples comprise metastatic tumors.   88. The method of any one of claims 83-87, wherein the one or more samples comprise bodily fluid.   89. The method of any one of claims 83 to 88, wherein the one or more samples comprise a cell free sample.   89. The method of any one of claims 83 to 88, wherein the one or more samples comprise lymphocytes.   91. The method of any one of claims 83-90, wherein the one or more samples comprise plasma.   92. The method according to any one of claims 83 to 91, wherein the identification of the putative variant comprises comparing the genomic sequence to a sequence from a bank of sequences obtained from one or more previously analyzed patients. the method of.   93. The scoring according to any one of claims 83 to 92, wherein said scoring of said putative variants comprises adjusting probabilities based on machine learning methods trained with correct and false call pairs. Method.   94. The method of any one of claims 83 to 93, wherein the identifying and scoring of the putative variant comprises making an inference at a chromosomal locus.   95. The method of claim 94, wherein said creating of inference comprises using a probabilistic model.   96. The method of claim 94 or 95, wherein said creating of inferences comprises using statistical inference.   97. The method of any one of claims 94 to 96, wherein said creating of inference comprises using Bayesian inference.   100. A method according to any one of claims 94 to 97, wherein said creating of inference comprises using a Bayesian network model.   100. The method of any one of claims 94 to 98, wherein said generating of inferences is based on prior probabilities of finding germline and somatic variants.   100. The method of any one of claims 94 to 99, wherein the generation of inference is based on a series of sequence reads aligned across the chromosomal loci.   101. The method according to any one of claims 94 to 100, wherein said creating of inferences is based on an error rate of said high throughput sequencing equipment.   102. The method of claim 101, wherein the error rate is provided in quality assurance for a base call.   103. A method according to any one of claims 94 to 102, wherein the generation of inference is based on a multiple relationship of chromosomal regions across the chromosomal locus.   104. The method of any one of claims 94 to 103, wherein said generating of inferences is based on a process model of clonal evolution of cancer.   105. The method according to any one of claims 94 to 104, wherein said generating of inferences is based on calls at chromosomal loci derived from one or more other samples of said individual.   106. The method according to any one of claims 94 to 105, wherein said generating of inferences is based on calls at chromosomal loci derived from one or more samples of one or more other individuals.   107. The method according to any one of claims 94 to 106, wherein said generating of inferences is based on prior knowledge of common polymorphisms at chromosomal loci of one or more reference populations.   108. The method of any one of claims 94-107, wherein said generating of inferences is based on prior knowledge of mutations of one or more recurrent cancers at chromosomal loci.   109. The method of any one of claims 94 to 108, wherein said generating of inferences is based on the proportion of cancer cells in a sample containing cancer.   110. The method of claim 109, wherein the sample containing cancer comprises one or more DNA molecules that cause cancer.   110. The method of claim 109, wherein the sample comprising cancer comprises one or more cancerous tissues.   113. A method according to any one of claims 94 to 111, wherein said creating of inference comprises describing a variant by means of a probabilistic model.   113. The method according to any one of claims 94 to 112, wherein said creating of inference comprises describing a series of aligned sequence reads across said chromosomal loci by means of a probabilistic model.   114. A method according to any one of claims 94 to 113, wherein said creating of inference comprises describing a multiple relationship at said chromosomal locus by means of a probabilistic model.   115. The method according to any one of claims 94 to 114, wherein said creating of inference comprises describing the proportion of cancer cells in a sample by means of a probabilistic model.   116. The method of claim 115, wherein the ratio is described by a binary variable.   One or more coding regions, expected severity of damage, one or more germline mutations, one or more somatic mutations, interactions between one or more mutations and drugs, in clinical trials Annotating the putative variant as to the effect on one or more of the one or more mutations, one or more diseases, one or more symptoms, or one or more side effects observed 117. The method of any one of claims 83-116.   118. The method of any one of claims 83-117, further comprising the step of recommending a treatment method to the individual.   119. The method of any one of claims 83 to 118, further comprising the step of recommending a treatment method to the individual.   120. A method according to any one of claims 83 to 119, further comprising the step of assessing the progress of treatment of the individual.   121. A method according to any one of claims 83 to 120, further comprising the step of assessing risk.   123. A method according to any one of claims 83 to 121, further comprising the step of monitoring the efficiency of a therapeutic method administered to the individual.   124. The method of any one of claims 83 to 122, further comprising the step of monitoring the efficiency of a treatment method administered to the individual.